Citations

Here is a list of scientific publications citing Alvascience’s software solutions:

2024

Kumar, A., Ojha, P. K., & Roy, K. (2024). First report on pesticide sub-chronic and chronic toxicities against dogs using QSAR and chemical read-across. SAR and QSAR in Environmental Research, 35(3), 241–263. https://doi.org/10.1080/1062936X.2024.2320143

Xin, L., Yu, H., Liu, S., Ying, G., & Chen, C. (2024). POPs identification using simple low-code machine learning. Science of The Total Environment, 921(December 2023), 171143. https://doi.org/10.1016/j.scitotenv.2024.171143

Gomatam, A., Umesh, B., Krishan, H., Singh, D., & Suryanarayana, U. (2024). Improved QSAR models for PARP ‑ 1 inhibition using data balancing , interpretable machine learning , and matched molecular pair analysis. Molecular Diversity, 0123456789. https://doi.org/10.1007/s11030-024-10809-9

Mondal, I., Halder, A. K., Pattanayak, N., Mandal, S. K., & Cordeiro, M. N. D. S. (2024). Shaping the Future of Obesity Treatment: In Silico Multi-Modeling of IP6K1 Inhibitors for Obesity and Metabolic Dysfunction. Pharmaceuticals, 17(2), 263. https://doi.org/10.3390/ph17020263

Torigoe, T., Takahashi, M., Heravizadeh, O., Ikeda, K., Nakatani, K., Bamba, T., & Izumi, Y. (2024). Predicting Retention Time in Unified-Hydrophilic-Interaction/Anion-Exchange Liquid Chromatography High-Resolution Tandem Mass Spectrometry (Unified-HILIC/AEX/HRMS/MS) for Comprehensive Structural Annotation of Polar Metabolome. Analytical Chemistry, 96(3), 1275–1283. https://doi.org/10.1021/acs.analchem.3c04618

Makarov, D. M., Kalikin, N. N., & Budkov, Y. A. (2024). Prediction of Drug-like Compounds Solubility in Supercritical Carbon Dioxide: A Comparative Study between Classical Density Functional Theory and Machine Learning Approaches. Industrial & Engineering Chemistry Research, 63(3), 1589–1603. https://doi.org/10.1021/acs.iecr.3c03208

Naveja, J. J., Saldívar‐González, F. I., Prado‐Romero, D. L., Ruiz‐Moreno, A. J., Velasco‐Velázquez, M., Miranda‐Quintana, R. A., & Medina‐Franco, J. L. (2024). Visualization, Exploration, and Screening of Chemical Space in Drug Discovery. In Computational Drug Discovery (pp. 365–393). Wiley. https://doi.org/10.1002/9783527840748.ch16

Wang, Z., Han, M., & Jin, B. (2024). Identifying Candidate Persistent, Mobile, and Toxic (PMT) and Very Persistent and Very Mobile (vPvM) Substances in Shale Gas Drilling Fluids by Combining Nontarget Analysis and Machine Learning Model. Environmental Science & Technology Letters. https://doi.org/10.1021/acs.estlett.3c00943

Roshan, T. A., & Behera, R. N. (2024). Solubility of CO2 in ionic liquids: Predictions based on QSPR study with artificial neural network. In International Conference on Recent Trends in Composite Sciences with Computational Analysis (p. 040002). Dehradun, India: AIP Publishing. https://doi.org/10.1063/5.0182967

Ghosh, V., Bhattacharjee, A., Kumar, A., & Ojha, P. K. (2024). q-RASTR modelling for prediction of diverse toxic chemicals towards T. pyriformis. SAR and QSAR in Environmental Research, 1–20. https://doi.org/10.1080/1062936X.2023.2298452

Kumar, V., Banerjee, A., & Roy, K. (2024). Machine learning-based q-RASAR approach for the in silico identification of novel multi-target inhibitors against Alzheimer’s disease. Chemometrics and Intelligent Laboratory Systems, 245(December 2023), 105049. https://doi.org/10.1016/j.chemolab.2023.105049

Li, Y., Tao, C., Fu, D., Jafvert, C. T., & Zhu, T. (2024). Integrating molecular descriptors for enhanced prediction: Shedding light on the potential of pH to model hydrated electron reaction rates for organic compounds. Chemosphere, 349(December 2023), 140984. https://doi.org/10.1016/j.chemosphere.2023.140984

Gallagher, A., & Kar, S. (2024). Unveiling first report on in silico modeling of aquatic toxicity of organic chemicals to Labeo rohita (Rohu) employing QSAR and q-RASAR. Chemosphere, 349(October 2023), 140810. https://doi.org/10.1016/j.chemosphere.2023.140810

Seo, M., Choi, J., Park, J., Yu, W., & Kim, S. (2024). Computational modeling approaches for developing a synergistic effect prediction model of estrogen agonistic activity. Chemosphere, 349(September 2023), 140926. https://doi.org/10.1016/j.chemosphere.2023.140926

2023

Muñoz-Vega, M. C., López-Hernández, S., Sierra-Chavarro, A., Scotti, M. T., Scotti, L., Coy-Barrera, E., & Herrera-Acevedo, C. (2023). Machine-Learning- and Structure-Based Virtual Screening for Selecting Cinnamic Acid Derivatives as Leishmania major DHFR-TS Inhibitors. Molecules, 29(1), 179. https://doi.org/10.3390/molecules29010179

Dichiara, M., Cosentino, G., Giordano, G., Pasquinucci, L., Marrazzo, A., Costanzo, G., & Amata, E. (2023). Designing drugs optimized for both blood–brain barrier permeation and intra-cerebral partition. Expert Opinion on Drug Discovery, 1–13. https://doi.org/10.1080/17460441.2023.2294118

Ghosh, S., Chatterjee, M., & Roy, K. (2023). Quantitative Read-across structure-activity relationship (q-RASAR): A new approach methodology to model aquatic toxicity of organic pesticides against different fish species. Aquatic Toxicology, 265(October), 106776. https://doi.org/10.1016/j.aquatox.2023.106776

Nath, A., Ojha, P. K., & Roy, K. (2023). QSAR assessment of aquatic toxicity potential of diverse agrochemicals. SAR and QSAR in Environmental Research, 34(11), 923–942. https://doi.org/10.1080/1062936X.2023.2278074

Zheng, J., Yang, J., Zhao, F., Peng, B., Wang, Y., & Fang, M. (2023). CIL-ExPMRM: An Ultrasensitive Chemical Isotope Labeling Assisted Pseudo-MRM Platform to Accelerate Exposomic Suspect Screening. Environmental Science & Technology, 57(30), 10962–10973. https://doi.org/10.1021/acs.est.3c01830

Trinh, C., Lasala, S., Herbinet, O., & Meimaroglou, D. (2023). On the Development of Descriptor-Based Machine Learning Models for Thermodynamic Properties: Part 2—Applicability Domain and Outliers. Algorithms, 16(12), 573. https://doi.org/10.3390/a16120573

Prado-Romero, D. L., Gómez-García, A., Cedillo-González, R., Villegas-Quintero, H., Avellaneda-Tamayo, J. F., López-López, E., Saldívar-González, F. I., Chávez-Hernández, A. L., & Medina-Franco, J. L. (2023). Consensus docking aid to model the activity of an inhibitor of DNA methyltransferase 1 inspired by de novo design. Frontiers in Drug Discovery, 3(December), 1–14. https://doi.org/10.3389/fddsv.2023.1261094

McGibbon, M., Shave, S., Dong, J., Gao, Y., Houston, D. R., Xie, J., … Blay, V. (2023). From intuition to AI: evolution of small molecule representations in drug discovery. Briefings in Bioinformatics, 25(1), 1–13. https://doi.org/10.1093/bib/bbad422

Trinh, C., Tbatou, Y., Lasala, S., Herbinet, O., & Meimaroglou, D. (2023). On the Development of Descriptor-Based Machine Learning Models for Thermodynamic Properties: Part 1—From Data Collection to Model Construction: Understanding of the Methods and Their Effects. Processes, 11(12), 3325. https://doi.org/10.3390/pr11123325

Darie, I.; Gosav, S.; Praisler, M. Characterisation of Novel Illicit Drugs Based on Computational Toxicology. In The 11th IEEE International Conference on E-Health and Bioengineering – EHB 2023; 2023; pp 23–26 http://www.ehbconference.ro/Portals/0/EHBWeb_2023_paper_16.pdf

Mora Lagares, L.; Vračko, M. Ecotoxicological Evaluation of Bisphenol A and Alternatives: A Comprehensive In Silico Modelling Approach. J. Xenobiotics 2023, 13 (4), 719–739. https://doi.org/10.3390/jox13040046

Pandey, S. K., & Roy, K. (2023). Development of a read-across-derived classification model for the predictions of mutagenicity data and its comparison with traditional QSAR models and expert systems. Toxicology, 500(October), 153676. https://doi.org/10.1016/j.tox.2023.153676

Sar, S., Mitra, S., Panda, P., Mandal, S. C., Ghosh, N., Halder, A. K., & Cordeiro, M. N. D. S. (2023). In Silico Modeling and Structural Analysis of Soluble Epoxide Hydrolase Inhibitors for Enhanced Therapeutic Design. Molecules, 28(17), 6379. https://doi.org/10.3390/molecules28176379

Ruan, T., Li, P., Wang, H., Li, T., & Jiang, G. (2023). Identification and Prioritization of Environmental Organic Pollutants: From an Analytical and Toxicological Perspective. Chemical Reviews, 123(17), 10584–10640. https://doi.org/10.1021/acs.chemrev.3c00056

Yan, Y.; Yang, F.; Zhang, H.; Pan, Y.; Ping, X.; Ge, Z. Study on Performance Evaluation Framework and Design/ Selection Guidelines of Working Fluids for Subcritical Organic Rankine Cycle from Molecular Structure Perspective. Energy 2023, 282 (June), 128582. https://doi.org/10.1016/j.energy.2023.128582

Mamada, H., Takahashi, M., Ogino, M., Nomura, Y., & Uesawa, Y. (2023). Predictive Models Based on Molecular Images and Molecular Descriptors for Drug Screening. ACS Omega, 8(40), 37186–37195. https://doi.org/10.1021/acsomega.3c04073

Yuting, L.; Fangrui, J.; Huoyu, R.; Zhanggao, L.; Zhenzhen, X. Correlation between the Onset Temperature and Molecular Descriptors of Organic Peroxides. Russ. J. Phys. Chem. A 2023, 97 (11), 2550–2558. https://doi.org/10.1134/S0036024423110195

Pandey, S. K.; Banerjee, A.; Roy, K. Machine Learning-Based q-RASPR Predictions of Detonation Heat for Nitrogen-Containing Compounds. Mater. Adv. 2023. https://doi.org/10.1039/D3MA00535F

Lee, H.; Liu, X.; An, J.; Wang, Y. Identification of Polymethoxyflavones (PMFs) from Orange Peel and Their Inhibitory Effects on the Formation of Trimethylamine (TMA) and Trimethylamine-N-Oxide (TMAO) Using CntA/B and CutC/D Enzymes and Molecular Docking. J. Agric. Food Chem. 2023. https://doi.org/10.1021/acs.jafc.3c04462

Mitra, S.; Chatterjee, S.; Bose, S.; Panda, P.; Basak, S.; Ghosh, N.; Mandal, S. C.; Singhmura, S.; Halder, A. K. Finding Structural Requirements of Structurally Diverse α-Glucosidase and α-Amylase Inhibitors through Validated and Predictive 2D-QSAR and 3D-QSAR Analyses. J. Mol. Graph. Model. 2024, 126 (September 2023), 108640. https://doi.org/10.1016/j.jmgm.2023.108640

Keefer, C. E.; Kauffman, G. W.; Gupta, R. R. Interpretable, Probability-Based Confidence Metric for Continuous Quantitative Structure-Activity Relationship Models. J. Chem. Inf. Model. 2013, 53 (2), 368–383. https://doi.org/10.1021/ci300554t

Szucs, R.; Brown, R.; Brunelli, C.; Hradski, J.; Masár, M. Impact of Structural Similarity on the Accuracy of Retention Time Prediction. J. Chromatogr. A 2023, 1707 (June), 464317. https://doi.org/10.1016/j.chroma.2023.464317

Cesaro, A.; Bagheri, M.; Torres, M.; Wan, F.; de la Fuente-Nunez, C. Deep Learning Tools to Accelerate Antibiotic Discovery. Expert Opin. Drug Discov. 2023, 1–13. https://doi.org/10.1080/17460441.2023.2250721

Noviandy, T. R.; Maulana, A.; Idroes, G. M.; Irvanizam, I.; Subianto, M.; Idroes, R. QSAR-Based Stacked Ensemble Classifier for Hepatitis C NS5B Inhibitor Prediction. In 2023 2nd International Conference on Computer System, Information Technology, and Electrical Engineering (COSITE); IEEE, 2023; pp 220–225. https://doi.org/10.1109/COSITE60233.2023.10250039

Adachi, A., Yamashita, T., Kanaya, S., & Kosugi, Y. (2023). Ensemble Machine Learning Approaches Based on Molecular Descriptors and Graph Convolutional Networks for Predicting the Efflux Activities of MDR1 and BCRP Transporters. AAPS Journal, 25(5). https://doi.org/10.1208/s12248-023-00853-y

Schiessler, E. J., Würger, T., Vaghefinazari, B., Lamaka, S. V, Meißner, R. H., Cyron, C. J., … Aydin, R. C. (2023). Searching the chemical space for effective magnesium dissolution modulators: a deep learning approach using sparse features. Npj Materials Degradation, 7(1), 74. https://doi.org/10.1038/s41529-023-00391-0

Han, M., Jin, B., Liang, J., Huang, C., & Arp, H. P. H. (2023). Developing machine learning approaches to identify candidate persistent, mobile and toxic (PMT) and very persistent and very mobile (vPvM) substances based on molecular structure. Water Research, 244(August), 120470. https://doi.org/10.1016/j.watres.2023.120470

Zhang, R., Chen, Z., Wang, B., Li, Y., Mu, Y., & Li, X. (2023). Modeling and Insights into the Structural Characteristics of Chemical Mitochondrial Toxicity. ACS Omega, 8(35), 31675–31682. https://doi.org/10.1021/acsomega.3c01725

Zhu, T., Li, S., Li, L., & Tao, C. (2023). A new perspective on predicting the reaction rate constants of hydrated electrons for organic contaminants: Exploring molecular structure characterization methods and ambient conditions. Science of The Total Environment, 904(August), 166316. https://doi.org/10.1016/j.scitotenv.2023.166316

Takeda, K., Takeuchi, K., Sakuratani, Y., & Kimbara, K. (2023). Optimal selection of learning data for highly accurate QSAR prediction of chemical biodegradability: a machine learning-based approach. SAR and QSAR in Environmental Research, 00(00), 1–15. https://doi.org/10.1080/1062936X.2023.2251889

Liu, S., & Kosugi, Y. (2023). Human Brain Penetration Prediction Using Scaling Approach from Animal Machine Learning Models. AAPS Journal, 25(5), 1–10. https://doi.org/10.1208/s12248-023-00850-1

Banerjee, A., & Roy, K. (2023). Read-across-based intelligent learning: development of a global q-RASAR model for the efficient quantitative predictions of skin sensitization potential of diverse organic chemicals. Environmental Science: Processes & Impacts. https://doi.org/10.1039/D3EM00322A

Motojima, K.; Sen, A.; Yamada, Y. M. A.; Kaneko, H. Catalyst Design and Feature Engineering to Improve Selectivity and Reactivity in Two Simultaneous Cross-Coupling Reactions. J. Chem. Inf. Model. 2023. https://doi.org/10.1021/acs.jcim.3c01196

Sosnowska, A.; Mudlaff, M.; Gorb, L.; Bulawska, N.; Zdybel, S.; Bakker, M.; Peijnenburg, W.; Puzyn, T. Expanding the Applicability Domain of QSPRs for Predicting Water Solubility and Vapor Pressure of PFAS. Chemosphere 2023, 340 (August), 139965. https://doi.org/10.1016/j.chemosphere.2023.139965

Gui, W.; Guo, H.; Wang, C.; Li, M.; Jin, Y.; Zhang, K.; Dai, J.; Zhao, Y. Comparative Developmental Toxicities of Zebrafish towards Structurally Diverse Per- and Polyfluoroalkyl Substances. Sci. Total Environ. 2023, 902 (August), 166569. https://doi.org/10.1016/j.scitotenv.2023.166569

Kartowikromo, K. Y.; Olajide, O. E.; Hamid, A. M. Collision Cross Section Measurement and Prediction Methods in Omics. J. Mass Spectrom. 2023, No. April. https://doi.org/10.1002/jms.4973

Elsayad, A. M., Zeghid, M., Ahmed, H. Y., & Elsayad, K. A. (2023). Exploration of Biodegradable Substances Using Machine Learning Techniques. Sustainability, 15(17), 12764. https://doi.org/10.3390/su151712764

Chatterjee, M., Banerjee, A., Tosi, S., Carnesecchi, E., Benfenati, E., & Roy, K. (2023). Machine learning – based q-RASAR modeling to predict acute contact toxicity of binary organic pesticide mixtures in honey bees. Journal of Hazardous Materials, 460(December 2022), 132358. https://doi.org/10.1016/j.jhazmat.2023.132358

Khan, P. M., Jillella, G. K., & Roy, K. (2023). Recent advancements in QSAR and machine learning approaches for risk assessment of organic chemicals. In QSAR in Safety Evaluation and Risk Assessment (pp. 167–185). Elsevier. https://doi.org/10.1016/B978-0-443-15339-6.00035-7

Wei, X., Li, B., Xiao, F., Yu, H., Ma, G., & Wang, X. (2023). Theoretical prediction for carrying capacity of microplastic toward organic pollutants. In QSAR in Safety Evaluation and Risk Assessment (pp. 447–457). Elsevier. https://doi.org/10.1016/B978-0-443-15339-6.00031-X

Zhu, T., Zhang, Y., Li, Y., Tao, T., & Tao, C. (2023). Contribution of molecular structures and quantum chemistry technique to root concentration factor: An innovative application of interpretable machine learning. Journal of Hazardous Materials, 459(May), 132320. https://doi.org/10.1016/j.jhazmat.2023.132320

Pan, Y., Yang, F., Zhang, H., Yan, Y., Ping, X., Yu, M., & Yang, A. (2023). New QSPR models for predicting critical temperature of binary organic mixtures using linear and nonlinear methods. Fluid Phase Equilibria, 575(June), 113916. https://doi.org/10.1016/j.fluid.2023.113916

Garcia Jimenez, D., Vallaro, M., Rossi Sebastiano, M., Apprato, G., D’Agostini, G., Rossetti, P., Ermondi, G., Caron, G. (2023). Chamelogk: A Chromatographic Chameleonicity Quantifier to Design Orally Bioavailable Beyond-Rule-of-5 Drugs. Journal of Medicinal Chemistry, 66(15), 10681–10693. https://doi.org/10.1021/acs.jmedchem.3c00823

Banerjee, A., & Roy, K. (2023). Prediction-Inspired Intelligent Training for the Development of Classification Read-across Structure–Activity Relationship (c-RASAR) Models for Organic Skin Sensitizers: Assessment of Classification Error Rate from Novel Similarity Coefficients. Chemical Research in Toxicology. https://doi.org/10.1021/acs.chemrestox.3c00155

Podder, T., Kumar, A., Bhattacharjee, A., & Ojha, P. K. (2023). Exploring regression-based QSTR and i-QSTR modeling for ecotoxicity prediction of diverse pesticides on multiple avian species. Environmental Science: Advances. https://doi.org/10.1039/D3VA00163F

Yang, S., & Kar, S. (2023). Application of artificial intelligence and machine learning in early detection of adverse drug reactions ( ADRs ) and drug-induced toxicity. Artificial Intelligence Chemistry, 1(2), 100011. https://doi.org/10.1016/j.aichem.2023.100011

Pan, Y., Yang, F., Zhang, H., Yan, Y., Ping, X., Yu, M., & Yang, A. (2023). New QSPR models for predicting critical temperature of binary organic mixtures using linear and nonlinear methods. Fluid Phase Equilibria, 575(June), 113916. https://doi.org/10.1016/j.fluid.2023.113916

Li, X., Vaghefinazari, B., Würger, T., Lamaka, S. V., Zheludkevich, M. L., & Feiler, C. (2023). Predicting corrosion inhibition efficiencies of small organic molecules using data-driven techniques. Npj Materials Degradation, 7(1), 64. https://doi.org/10.1038/s41529-023-00384-z

Rojas, C., Ballabio, D., Consonni, V., Suárez-Estrella, D., & Todeschini, R. (2023). Classification-based machine learning approaches to predict the taste of molecules: A review. Food Research International, 171(May), 113036. https://doi.org/10.1016/j.foodres.2023.113036

Sandoval, C., Torrens, F., Godoy, K., Reyes, C., & Farías, J. (2023). Application of Quantitative Structure-Activity Relationships in the Prediction of New Compounds with Anti-Leukemic Activity. International Journal of Molecular Sciences, 24(15), 12258. https://doi.org/10.3390/ijms241512258

Mitra, S.; Nandi, S.; Halder, A. K.; Cordeiro, M. N. D. S. SMILES-Based Bioactivity Descriptors to Model the Anti-Dengue Virus Activity: A Case Study; 2023; pp 117–136. https://doi.org/10.1007/978-3-031-28401-4_5

Nan, J., Zuo, S., Shi, H., Zhao, Y., Dai, J., & Zhang, K. (2023). Inverse relationship between transfer efficiencies into reproductive system and exposure concentration for organic pollutants: Implications for hazard assessment. Environmental Technology & Innovation, 32, 103282. https://doi.org/10.1016/j.eti.2023.103282

Ghosh, S., Chatterjee, M., & Roy, K. (2023). Predictive Quantitative Read-Across Structure–Property Relationship Modeling of the Retention Time (Log t R ) of Pesticide Residues Present in Foods and Vegetables. Journal of Agricultural and Food Chemistry, 71(24), 9538–9548. https://doi.org/10.1021/acs.jafc.3c01438

Kumar, A., Kumar, V., Podder, T., & Ojha, P. K. (2023). First report on ecotoxicological QSTR and i-QSTR modeling for the prediction of acute ecotoxicity of diverse organic chemicals against three protozoan species. Chemosphere, 335(May), 139066. https://doi.org/10.1016/j.chemosphere.2023.139066

Mohammadi, N., Abedanzadeh, S., Fereidonnejad, R., Mahdavinia, M., & Fereidoonnezhad, M. (2023). Effects of diphosphine ligands on the anticancer behavior of cycloplatinated(II) complexes of 2,2´-bipyridine N oxide: In vitro cytotoxicity, apoptosis, genotoxicity, and molecular docking studies. Journal of Organometallic Chemistry, 996, 122759. https://doi.org/10.1016/j.jorganchem.2023.122759

Chatterjee, M., & Roy, K. (2023). “Data fusion” quantitative read-across structure-activity-activity relationships (q-RASAARs) for the prediction of toxicities of binary and ternary antibiotic mixtures toward three bacterial species. Journal of Hazardous Materials, 459(May), 132129. https://doi.org/10.1016/j.jhazmat.2023.132129

Vatiwutipong, P., Vachmanus, S., Noraset, T., & Tuarob, S. (2023). Artificial Intelligence in Cosmetic Dermatology: A Systematic Literature Review. IEEE Access, 11(2), 71407–71425. https://doi.org/10.1109/ACCESS.2023.3295001

Zhang, R.; Wang, B.; Li, L.; Li, S.; Guo, H.; Zhang, P.; Hua, Y.; Cui, X.; Li, Y.; Mu, Y.; Huang, X.; Li, X. Modeling and Insights into the Structural Characteristics of Endocrine-Disrupting Chemicals. Ecotoxicol. Environ. Saf. 2023, 263 (January), 115251. https://doi.org/10.1016/j.ecoenv.2023.115251

Zhao, Y.; Mulder, R. J.; Houshyar, S.; Le, T. C. A Review on the Application of Molecular Descriptors and Machine Learning in Polymer Design. Polym. Chem. 2023. https://doi.org/10.1039/D3PY00395G

Gallagher, A.; Kar, S.; Sepúlveda, M. S. Computational Modeling of Human Serum Albumin Binding of Per- and Polyfluoroalkyl Substances Employing QSAR, Read-Across, and Docking. Molecules 2023, 28 (14), 5375. https://doi.org/10.3390/molecules28145375

dos Santos, B. R., Ramos, A. B. da S. B., de Menezes, R. P. B., Scotti, M. T., Colombo, F. A., Marques, M. J., & Reimão, J. Q. (2023). Repurposing the Medicines for Malaria Venture’s COVID Box to discover potent inhibitors of Toxoplasma gondii, and in vivo efficacy evaluation of almitrine bismesylate (MMV1804175) in chronically infected mice. PLOS ONE, 18(7), e0288335. https://doi.org/10.1371/journal.pone.0288335

Kajtazi, A., Russo, G., Wicht, K., Eghbali, H., & Lynen, F. (2023). Facilitating structural elucidation of small environmental solutes in RPLC-HRMS by retention index prediction. Chemosphere, 337(March), 139361. https://doi.org/10.1016/j.chemosphere.2023.139361

Xu, Y., Hu, Y., Ding, T., Wang, Z., Zhou, C., Zhu, Q., … Jiang, G. (2023). Novel macromolecular synthetic phenolic antioxidants in sludge on a national scale in China: Their distribution, potential transformation products, and ecological risk. Science of The Total Environment, 894(April), 164928. https://doi.org/10.1016/j.scitotenv.2023.164928

Zheng, H., Lv, W., Wang, Y., Feng, Y., & Yang, H. (2023). Molecular kinematic viscosity prediction of natural ester insulating oil based on sparse Machine learning models. Journal of Molecular Liquids, 385(March), 122355. https://doi.org/10.1016/j.molliq.2023.122355

Huang, P., Liu, S., Wang, Z., Ding, T., Tao, M., & Gu, Z. (2023). Study on the characterization of pesticide modes of action similarity and the multi-endpoint combined toxicity of pesticide mixtures to Caenorhabditis elegans. Science of The Total Environment, 893(February), 164918. https://doi.org/10.1016/j.scitotenv.2023.164918

Kumar, S., Jayan, J., Manoharan, A., Benny, F., Abdelgawad, M. A., Ghoneim, M. M., … Mathew, B. (2023). Discerning of isatin-based monoamine oxidase (MAO) inhibitors for neurodegenerative disorders by exploiting 2D, 3D-QSAR modelling and molecular dynamics simulation. Journal of Biomolecular Structure and Dynamics, 1–13. https://doi.org/10.1080/07391102.2023.2214216

Yang, S., Kar, S., & Leszczynski, J. (2023). Tools and software for computer-aided drug design and discovery. In Cheminformatics, QSAR and Machine Learning Applications for Novel Drug Development (pp. 637–661). Elsevier. https://doi.org/10.1016/B978-0-443-18638-7.00017-7

Chang, J., Zou, J., Lou, C., Ye, J., Feng, R., Li, Z., & Hu, G. (2023). Gas‐to‐ionic liquid partition: QSPR modeling and mechanistic interpretation. Molecular Informatics, (September 2022), 1–16. https://doi.org/10.1002/minf.202200223

Banerjee, A., & Roy, K. (2023). Machine-learning-based similarity meets traditional QSAR: “q-RASAR” for the enhancement of the external predictivity and detection of prediction confidence outliers in an hERG toxicity dataset. Chemometrics and Intelligent Laboratory Systems, 237(February), 104829. https://doi.org/10.1016/j.chemolab.2023.104829

Tripathi, M. K., Bhardwaj, B., Waiker, D. K., Tripathi, A., & Shrivastava, S. K. (2023). Discovery of novel dual acetylcholinesterase and butyrylcholinesterase inhibitors using machine learning and structure-based drug design. Journal of Molecular Structure, 1286(March), 135517. https://doi.org/10.1016/j.molstruc.2023.135517

SubLaban, A., Kessler, T. J., Van Dam, N., & Mack, J. H. (2023). Artificial Neural Network Models for Octane Number and Octane Sensitivity: A Quantitative Structure Property Relationship Approach to Fuel Design. Journal of Energy Resources Technology, 145(10). https://doi.org/10.1115/1.4062189

De Gauquier, P., Peeters, J., Vanommeslaeghe, K., Vander Heyden, Y., & Mangelings, D. (2023). Modelling the enantiorecognition of structurally diverse pharmaceuticals on O-substituted polysaccharide-based stationary phases. Talanta, 259(March), 124497. https://doi.org/10.1016/j.talanta.2023.124497

Bennett, S., & Jelfs, K. E. (2023). Porous Molecular Materials. In AI‐Guided Design and Property Prediction for Zeolites and Nanoporous Materials (pp. 251–282). Wiley. https://doi.org/10.1002/9781119819783.ch10

Zhou, Y., Tan, L., Zhang, X., & Zhao, S. (2023). Research on Prediction of Molecular Biological Activity Based on Graph Convolution. In Deep Learning Applications (pp. 243–273). WORLD SCIENTIFIC. https://doi.org/10.1142/9789811266911_0012

Paul, R., Roy, J., & Roy, K. (2023). Prediction of soil ecotoxicity against Folsomia candida using acute and chronic endpoints. SAR and QSAR in Environmental Research, 34(4), 321–340. https://doi.org/10.1080/1062936X.2023.2211350

Raza, A., Chohan, T. A., Buabeid, M., Arafa, E.-S. A., Chohan, T. A., Fatima, B., … Murtaza, G. (2022). Deep learning in drug discovery: a futuristic modality to materialize the large datasets for cheminformatics. Journal of Biomolecular Structure and Dynamics, 1–16. https://doi.org/10.1080/07391102.2022.2136244

Tran, T. T. Van, Surya Wibowo, A., Tayara, H., & Chong, K. T. (2023). Artificial Intelligence in Drug Toxicity Prediction: Recent Advances, Challenges, and Future Perspectives. Journal of Chemical Information and Modeling, 63(9), 2628–2643. https://doi.org/10.1021/acs.jcim.3c00200

Kumar, A., Ojha, P. K., & Roy, K. (2023). QSAR modeling of chronic rat toxicity of diverse organic chemicals. Computational Toxicology, 26(February), 100270. https://doi.org/10.1016/j.comtox.2023.100270

Gálvez‐Llompart, M., & Sastre, G. (2023). Machine Learning Search for Suitable Structure Directing Agents for the Synthesis of Beta (BEA) Zeolite Using Molecular Topology and Monte Carlo Techniques. In AI‐Guided Design and Property Prediction for Zeolites and Nanoporous Materials (pp. 61–80). Wiley. https://doi.org/10.1002/9781119819783.ch3

Nath, A., Ojha, P. K., & Roy, K. (2023). Computational modeling of aquatic toxicity of polychlorinated naphthalenes (PCNs) employing 2D-QSAR and chemical read-across. Aquatic Toxicology, 257(September 2022), 106429. https://doi.org/10.1016/j.aquatox.2023.106429

Desai, S. A., Deepak, M. S., Khandare, R. S., Sahu, A. R., Patel, V. K., Patel, A. S., … Patel, J. (2023). A QSAR STUDY AND MODEL DEVELOPMENT FOR TYROSINE KINASE INHIBITORS. Journal of Data Acquisition and Processing, 38(2), 3453–3467. https://doi.org/10.5281/zenodo.777218

Oubahmane, M., Hdoufane, I., Delaite, C., Sayede, A., Cherqaoui, D., & El Allali, A. (2023). Design of Potent Inhibitors Targeting the Main Protease of SARS-CoV-2 Using QSAR Modeling, Molecular Docking, and Molecular Dynamics Simulations. Pharmaceuticals, 16(4), 608. https://doi.org/10.3390/ph16040608

De, P., & Roy, K. (2023). Computational modeling of PET imaging agents for vesicular acetylcholine transporter (VAChT) protein binding affinity: application of 2D-QSAR modeling and molecular docking techniques. In Silico Pharmacology, 11(1), 9. https://doi.org/10.1007/s40203-023-00146-4

Idrovo‐Encalada, A. M., Rojas, A. M., Fissore, E. N., Tripaldi, P., Pis Diez, R., & Rojas, C. (2023). Chemoinformatic modelling of the antioxidant activity of phenolic compounds. Journal of the Science of Food and Agriculture, (January). https://doi.org/10.1002/jsfa.12561

Schieferdecker, S., & Vock, E. (2023). Development of Pharmacophore Models for the Important Off-Target 5-HT 2B Receptor. Journal of Medicinal Chemistry, 66(2), 1509–1521. https://doi.org/10.1021/acs.jmedchem.2c01679

Ciura, K., Fryca, I., & Gromelski, M. (2023). Prediction of the retention factor in cetyltrimethylammonium bromide modified micellar electrokinetic chromatography using a machine learning approach. Microchemical Journal, 187, 108393. https://doi.org/10.1016/j.microc.2023.108393

Apprato, G., D’Agostini, G., Rossetti, P., Ermondi, G., & Caron, G. (2023). In Silico Tools to Extract the Drug Design Information Content of Degradation Data: The Case of PROTACs Targeting the Androgen Receptor. Molecules, 28(3), 1206. https://doi.org/10.3390/molecules28031206

Kowalska, D., Sosnowska, A., Bulawska, N., Stępnik, M., Besselink, H., Behnisch, P., & Puzyn, T. (2023). How the Structure of Per- and Polyfluoroalkyl Substances (PFAS) Influences Their Binding Potency to the Peroxisome Proliferator-Activated and Thyroid Hormone Receptors—An In Silico Screening Study. Molecules, 28(2), 479. https://doi.org/10.3390/molecules28020479

Kumar, S., Manoharan, A., J, J., Abdelgawad, M. A., Mahdi, W. A., Alshehri, S., Ghoneim, M. M., Pappachen, L. K., Zachariah, S. M., Aneesh, T. P., & Mathew, B. (2023). Exploiting butyrylcholinesterase inhibitors through a combined 3-D pharmacophore modeling, QSAR, molecular docking, and molecular dynamics investigation. RSC Advances, 13(14), 9513–9529. https://doi.org/10.1039/D3RA00526G

Ksenofontov, A., Isaev, Y., Lukanov, M., Makarov, D. M., Eventova, V., Khodov, I., & Berezin, M. B. (2023). Accurate prediction of 11B NMR chemical shift of BODIPYs via machine learning. Physical Chemistry Chemical Physics, 19. https://doi.org/10.1039/D3CP00253E

Sharma, A., Kumar, R., & Varadwaj, P. K. (2022). Decoding Seven Basic Odors by Investigating Pharmacophores and Molecular Features of Odorants. Current Bioinformatics, 17(8), 759–774. https://doi.org/10.2174/1574893617666220519111254

Castillo-Garit, J. A., Cañizares-Carmenate, Y., Pham-The, H., Pérez-Doñate, V., Torrens, F., & Pérez-Giménez, F. (2023). A Review of Computational Approaches Targeting SARS-CoV-2 Main Protease to the Discovery of New Potential Antiviral Compounds. Current Topics in Medicinal Chemistry, 23(1), 3–16. https://doi.org/10.2174/2667387816666220426133555

Kumar, V., Saha, A., & Roy, K. (2023). Multi-target QSAR modeling for the identification of novel inhibitors against Alzheimer’s disease. Chemometrics and Intelligent Laboratory Systems, 233(August 2022), 104734. https://doi.org/10.1016/j.chemolab.2022.104734

Kumar, A., Podder, T., Kumar, V., & Ojha, P. K. (2023). Risk assessment of aromatic organic chemicals to T. pyriformis in environmental protection using regression-based QSTR and Read-Across algorithm. Process Safety and Environmental Protection, 170(September 2022), 842–854. https://doi.org/10.1016/j.psep.2022.12.067

Tao, L., He, J., Arbaugh, T., McCutcheon, J. R., & Li, Y. (2023). Machine learning prediction on the fractional free volume of polymer membranes. Journal of Membrane Science, 665(October 2022), 121131. https://doi.org/10.1016/j.memsci.2022.121131

Zhu, T., Chen, Y., & Tao, C. (2023). Multiple machine learning algorithms assisted QSPR models for aqueous solubility: Comprehensive assessment with CRITIC-TOPSIS. Science of The Total Environment, 857(September 2022), 159448. https://doi.org/10.1016/j.scitotenv.2022.159448

Gurumayum, S., Bharadwaj, S., Sheikh, Y., Barge, S. R., Saikia, K., Swargiary, D., Ahmed, S. A., Thakur, D., & Borah, J. C. (2023). Taxifolin-3-O-glucoside from Osbeckia nepalensis Hook. mediates antihyperglycemic activity in CC1 hepatocytes and in diabetic Wistar rats via regulating AMPK/G6Pase/PEPCK signaling axis. Journal of Ethnopharmacology, 303(September 2022), 115936. https://doi.org/10.1016/j.jep.2022.115936

Desai, S., Patel, V. K., Patel, A. S., & Patel, J. (2023). Development and Validation of an Easily Interpretable QSAR Model for Inhibitory Activity Prediction Against Dihydrofolate Reductase from Candida Albicans. Biological Forum – An International Journal, 15(1), 505–513.

Zhu, T., Yu, Y., & Tao, T. (2023). A comprehensive evaluation of liposome/water partition coefficient prediction models based on the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method: Challenges from different descriptor dimension reduction methods and machi. Journal of Hazardous Materials, 443(PA), 130181. https://doi.org/10.1016/j.jhazmat.2022.130181

Mitra, S., Halder, A. K., Ghosh, N., Mandal, S. C., & Cordeiro, M. N. D. S. (2023). Multi-model in silico characterization of 3-benzamidobenzoic acid derivatives as partial agonists of Farnesoid X receptor in the management of NAFLD. Computers in Biology and Medicine, 157(December 2022), 106789. https://doi.org/10.1016/j.compbiomed.2023.106789

2022

Mauri, A., & Bertola, M. (2022). Alvascience: A New Software Suite for the QSAR Workflow Applied to the Blood–Brain Barrier Permeability. International Journal of Molecular Sciences, 23(21), 12882. https://doi.org/10.3390/ijms232112882

Maloney, E. M., Villeneuve, D. L., Blackwell, B. R., Vitense, K., Corsi, S. R., Pronschinske, M. A., … Ankley, G. T. (2022). A framework for prioritizing contaminants in retrospective ecological assessments: Application in the Milwaukee Estuary (Milwaukee, WI). Integrated Environmental Assessment and Management, 00(00), 1–21. https://doi.org/10.1002/ieam.4725

Darie, I., & Praisler, M. (2022). Principal Component Analysis Assessing the Potential Clustering of 2C-x and DOx Amphetamines. 2022 E-Health and Bioengineering Conference (EHB), 01–04. https://doi.org/10.1109/EHB55594.2022.9991592

García Jiménez, D., Rossi Sebastiano, M., Vallaro, M., Mileo, V., Pizzirani, D., Moretti, E., Ermondi, G., & Caron, G. (2022). Designing Soluble PROTACs: Strategies and Preliminary Guidelines. Journal of Medicinal Chemistry. https://doi.org/10.1021/acs.jmedchem.2c00201

Chen, Z., Yang, B., Song, N., Chen, T., Zhang, Q., Li, C., Jiang, J., Chen, T., Yu, Y., & Liu, L. X. (2022). Machine learning-guided design of organic phosphorus-containing flame retardants to improve the limiting oxygen index of epoxy resins. Chemical Engineering Journal, July, 140547. https://doi.org/10.1016/j.cej.2022.140547

Huoyu, R., Zhiqiang, Z., Zhanggao, L., & Zhenzhen, X. (2022). QSPR models for the critical temperature and pressure of cycloalkanes. Chemical Physics Letters, 808(September), 140088. https://doi.org/10.1016/j.cplett.2022.140088

Miller, K. J., Thorpe, C., Eggenberger, A. L., Lee, K., Kang, M., Liu, F., Wang, K., & Jiang, S. (2022). Identifying Factors that Determine Effectiveness of Delivery Agents in Biolistic Delivery Using a Library of Amine-Containing Molecules. ACS Applied Bio Materials, 5(10), 4972–4980. https://doi.org/10.1021/acsabm.2c00689

Krmar, J., Svrkota, B., Đajić, N., Stojanović, J., Protić, A., & Otašević, B. (2022). Revealing Retention Mechanisms in Liquid Chromatography: QSRR Approach. In Chemometrics – Recent Advances, New Perspectives and Applications [Working Title]. IntechOpen. https://doi.org/10.5772/intechopen.106245

Ghosh, S., Chhabria, M. T., & Roy, K. (2022). Exploring quantitative structure–property relationship models for environmental fate assessment of petroleum hydrocarbons. Environmental Science and Pollution Research, 0123456789. https://doi.org/10.1007/s11356-022-23904-x

Shah, S., Arora, S., Chaple, D., Badne, P., Yende, S., Khonde, S., & Deshmukh, S. (2022). 2D-QSAR Modeling of Chalcone Analogues as Angiotensin Converting Enzyme Inhibitor. Biointerface Research in Applied Chemistry, 13(4), 370. https://doi.org/10.33263/BRIAC134.370

Desai, S. A. (2022). QSAR Regression Models for Predicting the Activity of Inhibitors of Staphylococcus Epidermidis. International Journal of Quantitative Structure-Property Relationships, 7(1), 1–17. https://doi.org/10.4018/IJQSPR.313712

Huoyu, R., Zhiqiang, Z., Guofang, J., Zhanggao, L., & Zhenzhen, X. (2022). Quantitative Structure-Property Relationship for Critical Temperature of Alkenes with Quantum-Сhemical and Topological Indices. Russian Journal of Physical Chemistry A, 96(11), 2329–2334. https://doi.org/10.1134/S0036024422110267

Salimi, A., Lim, J. H., Jang, J. H., & Lee, J. Y. (2022). The use of machine learning modeling, virtual screening, molecular docking, and molecular dynamics simulations to identify potential VEGFR2 kinase inhibitors. Scientific Reports, 12(1), 18825. https://doi.org/10.1038/s41598-022-22992-6

Würger, T., Wang, L., Snihirova, D., Deng, M., Lamaka, S. V., Winkler, D. A., Höche, D., Zheludkevich, M. L., Meißner, R. H., & Feiler, C. (2022). Data-driven selection of electrolyte additives for aqueous magnesium batteries. Journal of Materials Chemistry A, 10(40), 21672–21682. https://doi.org/10.1039/D2TA04538A

Makarov, D. M., Fadeeva, Y. A., Safonova, E. A., & Shmukler, L. E. (2022). Predictive modeling of antibacterial activity of ionic liquids by machine learning methods. Computational Biology and Chemistry, 101(July), 107775. https://doi.org/10.1016/j.compbiolchem.2022.107775

Desai, S., & Meshram, D. (2022). Development of Interpretable QSAR Model for Quick Screening of Inhibitors against Tyrosine Protein Kinase JAK-2. Chemical Science & Engineering Research, 4(100), 46–53. https://doi.org/10.36686/Ariviyal.CSER.2022.04.10.056

Banerjee, A., De, P., Kumar, V., Kar, S., & Roy, K. (2022). Quick and efficient quantitative predictions of androgen receptor binding affinity for screening Endocrine Disruptor Chemicals using 2D-QSAR and Chemical Read-Across. Chemosphere, 309(P1), 136579. https://doi.org/10.1016/j.chemosphere.2022.136579

Speck-Planche, A., & Kleandrova, V. V. (2022). The latest guidance on the simultaneous design of virtually active and non-hemolytic peptides. Expert Opinion on Drug Discovery, 1–3. https://doi.org/10.1080/17460441.2022.2128756

Ghosh, A., Panda, P., Halder, A. K., & Cordeiro, M. N. D. S. (2022). In silico characterization of aryl benzoyl hydrazide derivatives as potential inhibitors of RdRp enzyme of H5N1 influenza virus. Frontiers in Pharmacology, 13(September), 1–16. https://doi.org/10.3389/fphar.2022.1004255

Speck-Planche, A., & Kleandrova, V. V. (2022). Multi-Condition QSAR Model for the Virtual Design of Chemicals with Dual Pan-Antiviral and Anti-Cytokine Storm Profiles. ACS Omega, 7(36), 32119–32130. https://doi.org/10.1021/acsomega.2c03363

Rossi Sebastiano, M., Garcia Jimenez, D., Vallaro, M., Caron, G., & Ermondi, G. (2022). Refinement of Computational Access to Molecular Physicochemical Properties: From Ro5 to bRo5. Journal of Medicinal Chemistry, 65(18), 12068–12083. https://doi.org/10.1021/acs.jmedchem.2c00774

Chatterjee, M., & Roy, K. (2022). Chemical similarity and machine learning-based approaches for the prediction of aquatic toxicity of binary and multicomponent pharmaceutical and pesticide mixtures against Aliivibrio fischeri. Chemosphere, 308(P3), 136463. https://doi.org/10.1016/j.chemosphere.2022.136463

Schindler, K., Cortat, Y., Nedyalkova, M., Crochet, A., Lattuada, M., Pavic, A., & Zobi, F. (2022). Antimicrobial Activity of Rhenium Di- and Tricarbonyl Diimine Complexes: Insights on Membrane-Bound S. aureus Protein Binding. Pharmaceuticals, 15(9), 1107. https://doi.org/10.3390/ph15091107

Chen, J., Zhu, F., Qin, H., Song, Z., Qi, Z., & Sundmacher, K. (2022). Rational eutectic solvent design by linking regular solution theory with QSAR modelling. Chemical Engineering Science, 262, 118042. https://doi.org/10.1016/j.ces.2022.118042

Makarov, D. M., Fadeeva, Y. A., Shmukler, L. E., & Tetko, I. V. (2022). Machine learning models for phase transition and decomposition temperature of ionic liquids. Journal of Molecular Liquids, 366, 120247. https://doi.org/10.1016/j.molliq.2022.120247

Piekuś-Słomka, N., Zapadka, M., & Kupcewicz, B. (2022). Methoxy and methylthio-substituted trans-stilbene derivatives as CYP1B1 inhibitors – QSAR study with detailed interpretation of molecular descriptors. Arabian Journal of Chemistry, 15(11), 104204. https://doi.org/10.1016/j.arabjc.2022.104204

Kelleci Çelik, F., & Karaduman, G. (2022). In silico QSAR modeling to predict the safe use of antibiotics during pregnancy. Drug and Chemical Toxicology, 1–10. https://doi.org/10.1080/01480545.2022.2113888

Zhu, T., Tao, C., Cheng, H., & Cong, H. (2022). Versatile in silico modelling of microplastics adsorption capacity in aqueous environment based on molecular descriptor and machine learning. Science of The Total Environment, 846(May), 157455. https://doi.org/10.1016/j.scitotenv.2022.157455

Baba, H., Urano, R., Nagai, T., & Okazaki, S. (2022). Prediction of self‐diffusion coefficients of chemically diverse pure liquids by all‐atom molecular dynamics simulations. Journal of Computational Chemistry, July, 1–9. https://doi.org/10.1002/jcc.26975

Tayyebi, A., Alshami, A. S., Yu, X., & Kolodka, E. (2022). Can machine learning methods guide gas separation membranes fabrication? Journal of Membrane Science Letters, 2(2), 100033. https://doi.org/10.1016/j.memlet.2022.100033

Trinh, C., Meimaroglou, D., Lasala, S., & Herbinet, O. (2022). Machine Learning for the prediction of the thermochemical properties (enthalpy and entropy of formation) of a molecule from its molecular descriptors. In L. Montastruc & S. Negny (Eds.), 32nd European Symposium on Computer Aided Process Engineering (pp. 1471–1476). Elsevier. https://doi.org/10.1016/B978-0-323-95879-0.50246-0

Pal, S., Ghosh Dastidar, U., Ghosh, T., Ganguly, D., & Talukdar, A. (2022). Integration of Ligand-Based and Structure-Based Methods for the Design of Small-Molecule TLR7 Antagonists. Molecules, 27(13), 4026. https://doi.org/10.3390/molecules27134026

Amano, Y., Yamane, M., & Honda, H. (2022). RAID: Regression Analysis–Based Inductive DNA Microarray for Precise Read-Across. Frontiers in Pharmacology, 13(1223), 2022.02.15.480621. https://doi.org/10.3389/fphar.2022.879907

Liu, Y., Li, K., Huang, J., Yu, X., & Hu, W. (2022). Accurate Prediction of the Boiling Point of Organic Molecules by Multi-Component Heterogeneous Learning Model. Acta Chimica Sinica, 80(6), 714. https://doi.org/10.6023/A22010017

Tinkov, O. V., Grigorev, V. Y., Grigoreva, L. D., Osipov, V. N., Kolotaev, A. V., & Khachatryan, D. S. (2022). QSAR analysis and experimental evaluation of new quinazoline-containing hydroxamic acids as histone deacetylase 6 inhibitors. SAR and QSAR in Environmental Research, 33(7), 513–532. https://doi.org/10.1080/1062936X.2022.2092210

Costa, A. S., Martins, J. P. A., & de Melo, E. B. (2022). SMILES-based 2D-QSAR and similarity search for identification of potential new scaffolds for development of SARS-CoV-2 MPRO inhibitors. Structural Chemistry, 0123456789. https://doi.org/10.1007/s11224-022-02008-9

Zushi, Y. (2022). Direct Prediction of Physicochemical Properties and Toxicities of Chemicals from Analytical Descriptors by GC–MS. Analytical Chemistry, 94(25), 9149–9157. https://doi.org/10.1021/acs.analchem.2c01667

Cox, P. B., & Gupta, R. (2022). Contemporary Computational Applications and Tools in Drug Discovery. ACS Medicinal Chemistry Letters, 13(7), 1016–1029. https://doi.org/10.1021/acsmedchemlett.1c00662

Nkulikiyinka, P., Wagland, S. T., Manovic, V., & Clough, P. T. (2022). Prediction of Combined Sorbent and Catalyst Materials for SE-SMR, Using QSPR and Multitask Learning. Industrial & Engineering Chemistry Research, 61(26), 9218–9233. https://doi.org/10.1021/acs.iecr.2c00971

Bongaerts, N., Edoo, Z., Abukar, A. A., Song, X., Sosa-Carrillo, S., Haggenmueller, S., Savigny, J., Gontier, S., Lindner, A. B., & Wintermute, E. H. (2022). Low-cost anti-mycobacterial drug discovery using engineered E. coli. Nature Communications, 13(1), 3905. https://doi.org/10.1038/s41467-022-31570-3

Paul, R., Chatterjee, M., & Roy, K. (2022). First report on soil ecotoxicity prediction against Folsomia candida using intelligent consensus predictions and chemical read-across. Environmental Science and Pollution Research, 0123456789. https://doi.org/10.1007/s11356-022-21937-w

de Oliveira, A. M. (2022). Quantitative structure-activity relationships (QSARs). In Computer Aided Drug Design (CADD): From Ligand-Based Methods to Structure-Based Approaches (pp. 101–123). Elsevier. https://doi.org/10.1016/B978-0-323-90608-1.00007-1

Nath, A., & Roy, K. (2022). Chemometric modeling of acute toxicity of diverse aromatic compounds against Rana japonica. Toxicology in Vitro, 83(June), 105427. https://doi.org/10.1016/j.tiv.2022.105427

Huoyu, R., Zhiqiang, Z., Zhanggao, L., & Zhenzhen, X. (2022). Quantitative structure–property relationship for the critical temperature of saturated monobasic ketones, aldehydes, and ethers with molecular descriptors. International Journal of Quantum Chemistry, January, 1–10. https://doi.org/10.1002/qua.26950

Seddon, D., Müller, E. A., & Cabral, J. T. (2022). Machine learning hybrid approach for the prediction of surface tension profiles of hydrocarbon surfactants in aqueous solution. Journal of Colloid and Interface Science, 625, 328–339. https://doi.org/10.1016/j.jcis.2022.06.034

Yamane, J., Wada, T., Otsuki, H., Inomata, K., Suzuki, M., Hisaki, T., Sekine, S., Kouzuki, H., Kobayashi, K., Sone, H., Yamashita, J. K., Osawa, M., Saito, M. K., & Fujibuchi, W. (2022). StemPanTox: A fast and wide-target drug assessment system for tailor-made safety evaluations using personalized iPS cells. IScience, 25(7), 104538. https://doi.org/10.1016/j.isci.2022.104538

Pastewska, M., Żołnowska, B., Kovačević, S., Kapica, H., Gromelski, M., Stoliński, F., Sławiński, J., Sawicki, W., & Ciura, K. (2022). Modeling of Anticancer Sulfonamide Derivatives Lipophilicity by Chemometric and Quantitative Structure-Retention Relationships Approaches. Molecules, 27(13), 3965. https://doi.org/10.3390/molecules27133965

De, P., Kumar, V., Kar, S., Roy, K., & Leszczynski, J. (2022). Repurposing FDA approved drugs as possible anti-SARS-CoV-2 medications using ligand-based computational approaches: sum of ranking difference-based model selection. Structural Chemistry, 0123456789. https://doi.org/10.1007/s11224-022-01975-3

Galvez-Llompart, M., Zanni, R., Galvez, J., Basak, S. C., & Goyal, S. M. (2022). COVID-19 and the Importance of Being Prepared: A Multidisciplinary Strategy for the Discovery of Antivirals to Combat Pandemics. Biomedicines, 10(6), 1342. https://doi.org/10.3390/biomedicines10061342

García, C. A., Gil-de-la-Fuente, A., Barbas, C., & Otero, A. (2022). Probabilistic metabolite annotation using retention time prediction and meta-learned projections. Journal of Cheminformatics, 14(1), 33. https://doi.org/10.1186/s13321-022-00613-8

Feng, Y., Singh, R., Chao, A., & Li, Y. (2022). Diagnostic Fragmentation Pathways for Identification of Phthalate Metabolites in Nontargeted Analysis Studies. Journal of the American Society for Mass Spectrometry, 33(6), 981–995. https://doi.org/10.1021/jasms.2c00052

Mamada, H., Nomura, Y., & Uesawa, Y. (2022). Novel QSAR Approach for a Regression Model of Clearance That Combines DeepSnap-Deep Learning and Conventional Machine Learning. ACS Omega, 7(20), 17055–17062. https://doi.org/10.1021/acsomega.2c00261

Vasighi, M., Romanova, J., & Nedyalkova, M. (2022). A multilevel approach for screening natural compounds as an antiviral agent for COVID-19. Computational Biology and Chemistry, 98(April), 107694. https://doi.org/10.1016/j.compbiolchem.2022.107694

Okuyama, M., Nakazawa, Y., & Funatsu, K. (2022). A data-driven scheme to search for alternative composite materials. Science and Technology of Advanced Materials: Methods, 2(1), 106–118. https://doi.org/10.1080/27660400.2022.2063009

Scior, T., Garcia-Hernandez, J. C., Abdallah, H. H., & Alexander, C. (2022). QSAR Applied to 4-Chloro-3-formylcoumarin Derivatives Targeting Human Thymidine Phosphorylase. Clinical Complementary Medicine and Pharmacology, 2(2), 100031. https://doi.org/10.1016/j.ccmp.2022.100031

Schieferdecker, S., Eberlein, A., Vock, E., & Beilmann, M. (2022). Development of an in silico consensus model for the prediction of the phospholipigenic potential of small molecules. Computational Toxicology, 22(January), 100226. https://doi.org/10.1016/j.comtox.2022.100226

Chatterjee, M., & Roy, K. (2022). Application of cross-validation strategies to avoid overestimation of performance of 2D-QSAR models for the prediction of aquatic toxicity of chemical mixtures. SAR and QSAR in Environmental Research, 1–22. https://doi.org/10.1080/1062936X.2022.2081255

Kim, J., Seo, M., Choi, J., & Na, M. (2022). MRA Toolbox v. 1.0: a web-based toolbox for predicting mixture toxicity of chemical substances in chemical products. Scientific Reports, 12(1), 8880. https://doi.org/10.1038/s41598-022-13028-0

Sumartha, I. G. A., Yuniarta, T. A., & Kesuma, D. (2022). QSAR STUDY OF PYRAZOLE-UREA HYBRID COMPOUNDS AS ANTIMALARIAL AGENT VIA PROLYL-tRNA SYNTHETASE INHIBITION. RASĀYAN J. Chem., 15(2), 1450–1460. http://doi.org/10.31788/RJC.2022.1526811

Sabbah, D. A., Samarat, H. H., Al‐Shalabi, E., Bardaweel, S. K., Hajjo, R., Sweidan, K., Khalaf, R. A., Al‐Zuheiri, A. M., & Abushaikha, G. (2022). Design, Synthesis, and Biological Examination of N‐ Phenyl‐6‐fluoro‐4‐hydroxy‐2‐quinolone‐3‐carboxamides as Anticancer Agents. ChemistrySelect, 7(19). https://doi.org/10.1002/slct.202200662

Sudhir A. Kulkarni and Kundan Ingale, CHAPTER 1:In Silico Approaches for Drug Repurposing for SARS-CoV-2 Infection , in The Coronavirus Pandemic and the Future: Virology, Epidemiology, Translational Toxicology and Therapeutics, Volume 2, 2022, pp. 1-80 https://doi.org/10.1039/9781839166839-00001

de Cripan, S. M., Cereto-Massagué, A., Herrero, P., Barcaru, A., Canela, N., & Domingo-Almenara, X. (2022). Machine Learning-Based Retention Time Prediction of Trimethylsilyl Derivatives of Metabolites. Biomedicines, 10(4), 879. https://doi.org/10.3390/biomedicines10040879

V. Kumar, S. Kar, P. De, K. Roy & J. Leszczynski (2022) Identification of potential antivirals against 3CLpro enzyme for the treatment of SARS-CoV-2: A multi-step virtual screening study, SAR and QSAR in Environmental Research, https://doi.org/10.1080/1062936X.2022.2055140

Chatterjee, M., & Roy, K. (2022). Recent Advances on Modelling the Toxicity of Environmental Pollutants for Risk Assessment: from Single Pollutants to Mixtures. Current Pollution Reports, 0123456789. https://doi.org/10.1007/s40726-022-00219-6

Khan, H. A., & Jabeen, I. (2022). Combined Machine Learning and GRID-Independent Molecular Descriptor (GRIND) Models to Probe the Activity Profiles of 5-Lipoxygenase Activating Protein Inhibitors. Frontiers in Pharmacology, 13(March), 1–15. https://doi.org/10.3389/fphar.2022.825741

Rojas, C., Ballabio, D., Pacheco Sarmiento, K., Pacheco Jaramillo, E., Mendoza, M., & García, F. (2022). ChemTastesDB: A curated database of molecular tastants. Food Chemistry: Molecular Sciences, 4, 100090. https://doi.org/10.1016/j.fochms.2022.100090

Sun, X., Zhang, X., Wang, L., Li, Y., Muir, D. C. G., & Zeng, E. Y. (2022). Towards a better understanding of deep convolutional neural network processes for recognizing organic chemicals of environmental concern. Journal of Hazardous Materials, 421(July 2021), 126746. https://doi.org/10.1016/j.jhazmat.2021.126746

Lee, J., Song, S. Bin, Chung, Y. K., Jang, J. H., & Huh, J. (2022). BoostSweet: Learning molecular perceptual representations of sweeteners. Food Chemistry, 383(September 2021), 132435. https://doi.org/10.1016/j.foodchem.2022.132435

Song, X.-C., Dreolin, N., Damiani, T., Canellas, E., & Nerin, C. (2022). Prediction of Collision Cross Section Values: Application to Non-Intentionally Added Substance Identification in Food Contact Materials. Journal of Agricultural and Food Chemistry, 70(4), 1272–1281. https://doi.org/10.1021/acs.jafc.1c06989

Halder, A. K., Delgado, A. H. S., & Cordeiro, M. N. D. S. (2022). First multi-target QSAR model for predicting the cytotoxicity of acrylic acid-based dental monomers. Dental Materials, 38(2), 333–346. https://doi.org/10.1016/j.dental.2021.12.014

Rusanov, A. I., Dmitrieva, O. A., Mamardashvili, N. Z., & Tetko, I. V. (2022). More Is Not Always Better: Local Models Provide Accurate Predictions of Spectral Properties of Porphyrins. International Journal of Molecular Sciences, 23(3), 1201. https://doi.org/10.3390/ijms23031201

Baskin, I., Epshtein, A., & Ein-Eli, Y. (2022). Benchmarking machine learning methods for modeling physical properties of ionic liquids. Journal of Molecular Liquids, 351, 118616. https://doi.org/10.1016/j.molliq.2022.118616

Si-Hung, L., Izumi, Y., Nakao, M., Takahashi, M., & Bamba, T. (2022). Investigation of supercritical fluid chromatography retention behaviors using quantitative structure-retention relationships. Analytica Chimica Acta, 1197, 339463. https://doi.org/10.1016/j.aca.2022.339463

Morishita, T., & Kaneko, H. (2022). Development of Prediction Models for the Self-Accelerating Decomposition Temperature of Organic Peroxides. ACS Omega, 7(2), 2429–2437. https://doi.org/10.1021/acsomega.1c06481

Oztan Akturk, S., Tugcu, G., & Sipahi, H. (2022). Development of a QSAR model to predict comedogenic potential of some cosmetic ingredients. Computational Toxicology, 21(June 2021), 100207. https://doi.org/10.1016/j.comtox.2021.100207

Schmidt, S., Schindler, M., & Eriksson, L. (2022). Block‐wise exploration of molecular descriptors with Multi‐block Orthogonal Component Analysis (MOCA). Molecular Informatics, 2100165, 2100165. https://doi.org/10.1002/minf.202100165

Ksenofontov, A. A., Lukanov, M. M., Bocharov, P. S., Berezin, M. B., & Tetko, I. V. (2022). Deep neural network model for highly accurate prediction of BODIPYs absorption. Spectrochimica Acta – Part A: Molecular and Biomolecular Spectroscopy, 267(Part 2), 120577. https://doi.org/10.1016/j.saa.2021.120577

Mukherjee, R. K., Kumar, V., & Roy, K. (2022). Chemometric modeling of plant protection products ( PPPs ) for the prediction of acute contact toxicity against honey bees ( A . mellifera ): A 2D-QSAR approach. Journal of Hazardous Materials, 423(PB), 127230. https://doi.org/10.1016/j.jhazmat.2021.127230

Vakarelska, E., Nedyalkova, M., Vasighi, M., & Simeonov, V. (2022). Persistent organic pollutants (POPs) – QSPR classification models by means of Machine learning strategies. Chemosphere, 287(P2), 132189. https://doi.org/10.1016/j.chemosphere.2021.132189

Zhu, T., & Tao, C. (2021). Prediction models with multiple machine learning algorithms for POPs: the calculation of PDMS-air partition coefficient from molecular descriptor. Journal of Hazardous Materials, 423(PB), 127037. https://doi.org/10.1016/j.jhazmat.2021.127037

Galvez-Llompart, M., Zanni, R., Garcia-Domenech, R., & Galvez, J. (2022). How Molecular Topology Can Help in Amyotrophic Lateral Sclerosis (ALS) Drug Development: A Revolutionary Paradigm for a Merciless Disease. Pharmaceuticals, 15(1), 94. https://doi.org/10.3390/ph15010094

Mukherjee, R. K., Kumar, V., & Roy, K. (2022). Ecotoxicological QSTR and QSTTR Modeling for the Prediction of Acute Oral Toxicity of Pesticides against Multiple Avian Species. Environmental Science & Technology, 56(1), 335–348. https://doi.org/10.1021/acs.est.1c05732

Rojas, C., Alcívar León, C. D., Contreras Aguilar, E., Mazón Ayala, P. V., & Muñoz, D. (2022). Quantitative Structure–Property Relationship for the Retention Index of Volatile and Semi-Volatile Compounds of Coffee. Chemistry Proceedings, 8(48). https://doi.org/10.3390/ecsoc-25-11731

2021

Aleksić, S., Seeliger, D., & Brown, J. B. (2021). ADMET Predictability at Boehringer Ingelheim: State‐of‐the‐Art, and Do Bigger Datasets or Algorithms Make a Difference? Molecular Informatics, 2100113(40), 2100113. https://doi.org/10.1002/minf.202100113

Kanai, C., Kawasaki, E., Murakami, R., Morita, Y., & Yoshimori, A. (2021). Computational Prediction of Compound–Protein Interactions for Orphan Targets Using CGBVS. Molecules, 26(17), 5131. https://doi.org/10.3390/molecules26175131

Garcia Jimenez, D., Rossi Sebastiano, M., Caron, G., & Ermondi, G. (2021). Are we ready to design oral PROTACs®? ADMET and DMPK, 9(4), 243–254. https://doi.org/10.5599/admet.1037

Saçan, M.T., Önlü, S. and Tugcu, G. (2021). Chemometric Modeling of Algal Toxicity. In Chemometrics and Cheminformatics in Aquatic Toxicology, K. Roy (Ed.). https://doi.org/10.1002/9781119681397.ch14

Nedyalkova, M., Vasighi, M., Sappati, S., Kumar, A., Madurga, S., & Simeonov, V. (2021). Inhibition Ability of Natural Compounds on Receptor-Binding Domain of SARS-CoV2: An In Silico Approach. Pharmaceuticals, 14(12), 1328. https://doi.org/10.3390/ph14121328

Schiessler, E. J., Würger, T., Lamaka, S. V, Meißner, R. H., Cyron, C. J., Zheludkevich, M. L., Feiler, C., & Aydin, R. C. (2021). Predicting the inhibition efficiencies of magnesium dissolution modulators using sparse machine learning models. Npj Computational Materials, 7(1), 193. https://doi.org/10.1038/s41524-021-00658-7

Tiihonen, A., Cox-Vazquez, S. J., Liang, Q., Ragab, M., Ren, Z., Hartono, N. T. P., Liu, Z., Sun, S., Zhou, C., Incandela, N. C., Limwongyut, J., Moreland, A. S., Jayavelu, S., Bazan, G. C., & Buonassisi, T. (2021). Predicting Antimicrobial Activity of Conjugated Oligoelectrolyte Molecules via Machine Learning. Journal of the American Chemical Society, 143(45), 18917–18931. https://doi.org/10.1021/jacs.1c05055

Laidi, M., Abdallah, E., Si-Moussa, C., Benkortebi, O., Hentabli, M., & Hanini, S. (2021). CMC of diverse Gemini surfactants modelling using a hybrid approach combining SVR-DA. Chemical Industry and Chemical Engineering Quarterly, 27(3), 299–312. https://doi.org/10.2298/CICEQ200907048L

Halder, A. K., & Cordeiro, M. N. D. S. (2021). Multi-Target In Silico Prediction of Inhibitors for Mitogen-Activated Protein Kinase-Interacting Kinases. Biomolecules, 11(11), 1670. https://doi.org/10.3390/biom11111670

Falcón-Cano, G., Molina, C., & Cabrera-Pérez, M. A. (2021). ADME prediction with KNIME: A retrospective contribution to the second “Solubility Challenge.” ADMET and DMPK, 9(3), 209–218. https://doi.org/10.5599/admet.979

Ghosh, D., Koch, U., Hadian, K., Sattler, M., & Tetko, I. V. (2021). Highly Accurate Filters to Flag Frequent Hitters in AlphaScreen Assays by Suggesting their Mechanism. Molecular Informatics, 2100151, 2100151. https://doi.org/10.1002/minf.202100151

Zhu, T., Chen, W., Gu, Y., Jafvert, C. T., & Fu, D. (2021). Polyethylene-water partition coefficients for polychlorinated biphenyls: Application of QSPR predictions models with experimental validation. Water Research, 207(June), 117799. https://doi.org/10.1016/j.watres.2021.117799

Alsenan, S. A. (2021). An Empirical Comparison of Machine and Deep Learning Algorithms’ Performance on Chemical Data. The 23rd International Conference on Information Integration and Web Intelligence, 655–658. https://doi.org/10.1145/3487664.3487756

Zhu, T., Chen, W., Jafvert, C. T., Fu, D., Cheng, H., Chen, M., & Wang, Y. (2021). Development of novel experimental and modelled low density polyethylene (LDPE)-water partition coefficients for a range of hydrophobic organic compounds. Environmental Pollution, 291(September), 118223. https://doi.org/10.1016/j.envpol.2021.118223

Khan, P. M., & Roy, K. (2021). QSPR modelling for investigation of different properties of aminoglycoside-derived polymers using 2D descriptors. SAR and QSAR in Environmental Research, 32(7), 595–614. https://doi.org/10.1080/1062936X.2021.1939150

Mamada, H., Nomura, Y., & Uesawa, Y. (2021). Prediction Model of Clearance by a Novel Quantitative Structure–Activity Relationship Approach, Combination DeepSnap-Deep Learning and Conventional Machine Learning. ACS Omega, 6(36), 23570–23577. https://doi.org/10.1021/acsomega.1c03689

Aher, R. B., & Sarkar, D. (2021). 2D-QSAR modeling and two-fold classification of 1,2,4-triazole derivatives for antitubercular potency against the dormant stage of Mycobacterium tuberculosis. Molecular Diversity, 0123456789. https://doi.org/10.1007/s11030-021-10254-y

Casanova-Alvarez, O., Morales-Helguera, A., Cabrera-Pérez, M. Á., Molina-Ruiz, R., & Molina, C. (2021). A Novel Automated Framework for QSAR Modeling of Highly Imbalanced Leishmania High-Throughput Screening Data. Journal of Chemical Information and Modeling, 61(7), 3213–3231. https://doi.org/10.1021/acs.jcim.0c01439

Tinkov, O. V., Grigorev, V. Y., & Grigoreva, L. D. (2021). QSAR analysis of the acute toxicity of avermectins towards Tetrahymena pyriformis. SAR and QSAR in Environmental Research, 32(7), 541–571. https://doi.org/10.1080/1062936X.2021.1932583

Laidi, M., Abdallah, el, Si-Moussa, C., Benkortebi, O., Hentabli, M., & Hanini, S. (2021). CMC of diverse Gemini surfactants modelling using a hybrid approach combining SVR-DA. Chemical Industry and Chemical Engineering Quarterly, 27(3), 299–312. https://doi.org/10.2298/ciceq200907048l

Alsenan, S., Al-Turaiki, I., & Hafez, A. (2021). A deep learning approach to predict blood-brain barrier permeability. PeerJ Computer Science, 7, e515. https://doi.org/10.7717/peerj-cs.515

Gu, L., Zhu, T., & Chen, M. (2021). Modeling polyurethane foam (PUF)-air partition coefficients for persistent organic pollutants using linear and non-linear chemometric methods. Journal of Environmental Chemical Engineering, 9(4), 105615. https://doi.org/10.1016/j.jece.2021.105615

Feng, J.-J., Sun, X.-F., & Zeng, E. Y. (2021). Measurement of octanol–air partition coefficients for liquid crystals based on gas chromatography-retention time and its implication in predicting long-range transport potential. Chemosphere, 282(May), 131109. https://doi.org/10.1016/j.chemosphere.2021.131109

Galvez-Llompart, M., Ocello, R., Rullo, L., Stamatakos, S., Alessandrini, I., Zanni, R., Tuñón, I., Cavalli, A., Candeletti, S., Masetti, M., Romualdi, P., & Recanatini, M. (2021). Targeting the JAK/STAT Pathway: A Combined Ligand- and Target-Based Approach. Journal of Chemical Information and Modeling, 61(6), 3091–3108. https://doi.org/10.1021/acs.jcim.0c01468

Kleandrova, V. V., Scotti, L., Bezerra Mendonça Junior, F. J., Muratov, E., Scotti, M. T., & Speck-Planche, A. (2021). QSAR Modeling for Multi-Target Drug Discovery: Designing Simultaneous Inhibitors of Proteins in Diverse Pathogenic Parasites. Frontiers in Chemistry, 9(March), 1–20. https://doi.org/10.3389/fchem.2021.634663

Yoshimori, A., Kawasaki, E., Murakami, R., & Kanai, C. (2021). Discovery of Novel eEF2K Inhibitors Using HTS Fingerprint Generated from Predicted Profiling of Compound-Protein Interactions. Medicines, 8(5), 23. https://doi.org/10.3390/medicines8050023

Tao, L., Chen, G., & Li, Y. (2021). Machine learning discovery of high-temperature polymers. Patterns, 2(4), 100225. https://doi.org/10.1016/j.patter.2021.100225

Moussaoui, M., Laidi, M., Hanini, S., Abdallah, A. E. H., & Hentabli, M. (2021). Critical Properties and Acentric Factors of Pure Compounds Modelling Based on QSPR-SVM with Dragonfly Algorithm. Kemija u Industriji, 70(7–8), 375–386. https://doi.org/10.15255/KUI.2020.063

Huang, P., Liu, S., Xu, Y., Wang, Y., & Wang, Z. (2021). Combined lethal toxicities of pesticides with similar structures to Caenorhabditis elegans are not necessarily concentration additives. Environmental Pollution, 286(November 2020), 117207. https://doi.org/10.1016/j.envpol.2021.117207

Zanni, R., Galvez-Llompart, M., & Galvez, J. (2021). Computational analysis of macrolides as SARS-CoV-2 main protease inhibitors: a pattern recognition study based on molecular topology and validated by molecular docking. New Journal of Chemistry, 45(19), 8654–8675. https://doi.org/10.1039/D0NJ05983H

Bierling, A. L., Croy, I., Hummel, T., Cuniberti, G., & Croy, A. (2021). Olfactory Perception in Relation to the Physicochemical Odor Space. Brain Sciences, 11(5), 563. https://doi.org/10.3390/brainsci11050563

Kumar, R., Khan, F. U., Sharma, A., Siddiqui, M. H., Aziz, I. B., Kamal, M. A., Ashraf, G. M., Alghamdi, B. S., & Uddin, M. S. (2021). A deep neural network–based approach for prediction of mutagenicity of compounds. Environmental Science and Pollution Research. https://doi.org/10.1007/s11356-021-14028-9

Galvez, J., Zanni, R., Galvez-Llompart, M., & Benlloch, J. M. (2021). Macrolides May Prevent Severe Acute Respiratory Syndrome Coronavirus 2 Entry into Cells: A Quantitative Structure Activity Relationship Study and Experimental Validation. Journal of Chemical Information and Modeling, 61(4), 2016–2025. https://doi.org/10.1021/acs.jcim.0c01394

Liu, Y., Zhang, D., Tang, Y., Zhang, Y., Gong, X., Xie, S., & Zheng, J. (2021). Machine Learning-Enabled Repurposing and Design of Antifouling Polymer Brushes. Chemical Engineering Journal, 420(P1), 129872. https://doi.org/10.1016/j.cej.2021.129872

Szucs, R., Brown, R., Brunelli, C., Heaton, J. C., & Hradski, J. (2021). Structure Driven Prediction of Chromatographic Retention Times: Applications to Pharmaceutical Analysis. International Journal of Molecular Sciences, 22(8), 3848. https://doi.org/10.3390/ijms22083848

Schmidt, S., Schindler, M., Faber, D., & Hager, J. (2021). Fish early life stage toxicity prediction from acute daphnid toxicity and quantum chemistry. SAR and QSAR in Environmental Research, 1–24. https://doi.org/10.1080/1062936X.2021.1874514

Li, J., Wilkinson, J. L., & Boxall, A. B. A. (2021). Use of a large dataset to develop new models for estimating the sorption of active pharmaceutical ingredients in soils and sediments. Journal of Hazardous Materials, 415, 125688. https://doi.org/10.1016/j.jhazmat.2021.125688

Jiménez-Luna, J., Grisoni, F., Weskamp, N., & Schneider, G. (2021). Artificial intelligence in drug discovery: recent advances and future perspectives. Expert Opinion on Drug Discovery. https://doi.org/10.1080/17460441.2021.1909567

Halder, A. K., & Cordeiro, M. N. D. S. (2021). AKT Inhibitors: The Road Ahead to Computational Modeling-Guided Discovery. International Journal of Molecular Sciences, 22(8), 3944. https://doi.org/10.3390/ijms22083944

Nedyalkova, M., & Simeonov, V. (2021). Partitioning pattern of natural products based on molecular properties descriptors representing drug-likeness. Symmetry, 13(4). https://doi.org/10.3390/sym13040546

Mathew, S., Tess, D., Burchett, W., Chang, G., Woody, N., Keefer, C., Orozco, C., Lin, J., Jordan, S., Yamazaki, S., Jones, R., & Di, L. (2021). Evaluation of Prediction Accuracy for Volume of Distribution in Rat and Human Using In Vitro, In Vivo, PBPK and QSAR Methods. Journal of Pharmaceutical Sciences, 110(4), 1799–1823. https://doi.org/10.1016/j.xphs.2020.12.005

Zhu, T., Cao, Z., Prasad, R., Cheng, H., & Chen, M. (2021). In silico prediction of polyethylene-aqueous and air partition coefficients of organic contaminants using linear and nonlinear approaches. Journal of Environmental Management, 289, 112437. https://doi.org/10.1016/j.jenvman.2021.112437

Raimundo e Silva, J. P., Acevedo, C. A. H., de Souza, T. A., de Menezes, R. P. B., Sessions, Z. L., Abreu, L. S., Cibulski, S. P., Scotti, L., da Silva, M. S., Muratov, E. N., Scotti, M. T., & Tavares, J. F. (2021). Natural Products as Potential Agents Against SARS-CoV and SARS-CoV-2. Current Medicinal Chemistry, 28, 1–16. https://doi.org/10.2174/0929867328666210125113938

Seo, M., Chae, C. H., Lee, Y., Kim, H. R., & Kim, J. (2021). Novel QSAR Models for Molecular Initiating Event Modeling in Two Intersecting Adverse Outcome Pathways Based Pulmonary Fibrosis Prediction for Biocidal Mixtures. Toxics, 9(3), 59. https://doi.org/10.3390/toxics9030059

Ermondi, G., Garcia-jimenez, D., & Caron, G. (2021). PROTACs and Building Blocks : The 2D Chemical Space in Very Early Drug Discovery. 26(3), 672. https://doi.org/https://doi.org/10.3390/molecules26030672

Feng, C., Xu, Q., Qiu, X., Jin, Y., Ji, J., Lin, Y., Le, S., She, J., Lu, D., & Wang, G. (2021). Evaluation and application of machine learning-based retention time prediction for suspect screening of pesticides and pesticide transformation products in LC-HRMS. Chemosphere, 271, 129447. https://doi.org/10.1016/j.chemosphere.2020.129447

Kumar, A., Loharch, S., Kumar, S., Ringe, R. P., & Parkesh, R. (2021). Exploiting cheminformatic and machine learning to navigate the available chemical space of potential small molecule inhibitors of SARS-CoV-2. Computational and Structural Biotechnology Journal, 19, 424–438. https://doi.org/10.1016/j.csbj.2020.12.028

Aldosari, M. N., Yalamanchi, K. K., Gao, X., & Sarathy, S. M. (2021). Predicting entropy and heat capacity of hydrocarbons using machine learning. Energy and AI, 4, 100054. https://doi.org/10.1016/j.egyai.2021.100054

Sabbah, D. A., Al-Azaideh, B. A., Talib, W. H., Hajjo, R., Sweidan, K., Al-Zuheiri, A. M., Sheikha, G. A., & Shraim, S. (2021). New derivatives of sulfonylhydrazone as potential antitumor agents: Design, synthesis and cheminformatics evaluation. Acta Pharmaceutica, 71(4), 545–565. https://doi.org/10.2478/acph-2021-0043

Chen, S. T., Kowalewski, J., & Ray, A. (2021). Prolonged activation of carbon dioxide-sensitive neurons in mosquitoes. Interface Focus, 11(2), 20200043. https://doi.org/10.1098/rsfs.2020.0043

Liu, Y., Zhang, D., Tang, Y., Zhang, Y., Chang, Y., & Zheng, J. (2021). Machine Learning-Enabled Design and Prediction of Protein Resistance on Self-Assembled Monolayers and Beyond. ACS Applied Materials & Interfaces, 13(9), 11306–11319. https://doi.org/10.1021/acsami.1c00642

Sedykh, A. Y., Shah, R. R., Kleinstreuer, N. C., Auerbach, S. S., & Gombar, V. K. (2021). SAAGar−A new, extensible set of molecular substructures for QSAR/ QSPR and read-across predictions. Chemical Research in Toxicology. https://doi.org/10.1021/acs.chemrestox.0c00464

2020

Mauri, A. (2020). alvaDesc: A tool to calculate and analyze molecular descriptors and fingerprints. In K. Roy (Ed.), Ecotoxicological QSARs (pp. 801–820). Humana Press Inc. https://doi.org/10.1007/978-1-0716-0150-1_32

Falcón-Cano, G., Molina, C., & Cabrera-Pérez, M. Á. (2020). ADME Prediction with KNIME: Development and Validation of a Publicly Available Workflow for the Prediction of Human Oral Bioavailability. Journal of Chemical Information and Modeling, 60(6), 2660–2667. https://doi.org/10.1021/acs.jcim.0c00019

Di Pizio, A., Behr, J., & Krautwurst, D. (2020). Toward the Digitalization of Olfaction. In The Senses: A Comprehensive Reference (Vol. 3, pp. 758–768). Elsevier. https://doi.org/10.1016/B978-0-12-809324-5.24147-3

Alsenan, S. A., Al-Turaiki, I. M., & Hafez, A. M. (2020). Feature Extraction Methods in Quantitative Structure–Activity Relationship Modeling: A Comparative Study. IEEE Access, 8, 78737–78752. https://doi.org/10.1109/access.2020.2990375

Guo, Z., Huang, S., Wang, J., & Feng, Y. L. (2020). Recent advances in non-targeted screening analysis using liquid chromatography – high resolution mass spectrometry to explore new biomarkers for human exposure. Talanta, 219(July), 121339. https://doi.org/10.1016/j.talanta.2020.121339

Stošić, B., Janković, R., Stošić, M., Marković, D., Stanković, D., Sokolović, D., & Veselinović, A. M. (2020). In silico development of anesthetics based on barbiturate and thiobarbiturate inhibition of GABAA. Computational Biology and Chemistry, 88(March), 107318. https://doi.org/10.1016/j.compbiolchem.2020.107318

Falcón-Cano, G., Molina, C., & Cabrera-Pérez, M. A. (2020). ADME Prediction with KNIME: In silico aqueous solubility models based on supervised recursive machine learning approaches. ADMET and DMPK, 8(3), 251–273. https://doi.org/10.5599/admet.852

Zhu, T., Gu, Y., Cheng, H., & Chen, M. (2020). Versatile modelling of polyoxymethylene-water partition coefficients for hydrophobic organic contaminants using linear and nonlinear approaches. Science of the Total Environment, 728, 138881. https://doi.org/10.1016/j.scitotenv.2020.138881

Li, J., Carter, L. J., & Boxall, A. B. A. (2020). Evaluation and development of models for estimating the sorption behaviour of pharmaceuticals in soils. Journal of Hazardous Materials, 392(October 2019), 122469. https://doi.org/10.1016/j.jhazmat.2020.122469

Kowalewski, J., & Ray, A. (2020). Predicting novel drugs for SARS-CoV-2 using machine learning from a >10 million chemical space. Heliyon, 6(8), e04639. https://doi.org/10.1016/j.heliyon.2020.e04639

Sun, X., Zhang, X., Muir, D. C. G., & Zeng, E. Y. (2020). Identification of Potential PBT/POP-Like Chemicals by a Deep Learning Approach Based on 2D Structural Features. Environmental Science & Technology, 54(13), 8221–8231. https://doi.org/10.1021/acs.est.0c01437

Zhu, X., Ho, C. H., & Wang, X. (2020). Application of Life Cycle Assessment and Machine Learning for High-Throughput Screening of Green Chemical Substitutes. ACS Sustainable Chemistry and Engineering. https://doi.org/10.1021/acssuschemeng.0c02211

Kessler, T., St. John, P. C., Zhu, J., McEnally, C. S., Pfefferle, L. D., & Mack, J. H. (2020). A comparison of computational models for predicting yield sooting index. Proceedings of the Combustion Institute, 000, 1–9. https://doi.org/10.1016/j.proci.2020.07.009

George, A., & John, M. (2020). Impact of Oversampling on the Classification of Readily Biodegradable Materials. 2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), 1–5. https://doi.org/10.1109/ICDABI51230.2020.9325621

Zhu, T., Chen, W., Singh, R. P., & Cui, Y. (2020). Versatile in silico modeling of partition coefficients of organic compounds in polydimethylsiloxane using linear and nonlinear methods. Journal of Hazardous Materials, 399(February), 123012. https://doi.org/10.1016/j.jhazmat.2020.123012

Kleandrova, V. V., Scotti, M. T., Scotti, L., Nayarisseri, A., & Speck-Planche, A. (2020). Cell-based multi-target QSAR model for design of virtual versatile inhibitors of liver cancer cell lines. SAR and QSAR in Environmental Research, 00(00), 1–22. https://doi.org/10.1080/1062936X.2020.1818617

Rojas, C., Aranda, J. F., Pacheco Jaramillo, E., Losilla, I., Tripaldi, P., Duchowicz, P. R., & Castro, E. A. (2020). Foodinformatic prediction of the retention time of pesticide residues detected in fruits and vegetables using UHPLC/ESI Q-Orbitrap. Food Chemistry, (October), 128354. https://doi.org/10.1016/j.foodchem.2020.128354

Alsenan, S., Al-Turaiki, I., & Hafez, A. (2020). A Recurrent Neural Network model to predict blood–brain barrier permeability. Computational Biology and Chemistry, 89, 107377. https://doi.org/10.1016/j.compbiolchem.2020.107377

Tinkov, O., Polishchuk, P., Matveieva, M., Grigorev, V., Grigoreva, L., & Porozov, Y. (2020). The Influence of Structural Patterns on Acute Aquatic Toxicity of Organic Compounds. Molecular Informatics, 2000209, 1–14. https://doi.org/10.1002/minf.202000209

Zhu, T., Gu, L., Chen, M., & Sun, F. (2020). Exploring QSPR models for predicting PUF-air partition coefficients of organic compounds with linear and nonlinear approaches. Chemosphere, 266, 128962. https://doi.org/10.1016/j.chemosphere.2020.128962

Liu, A. L., Venkatesh, R., McBride, M., Reichmanis, E., Meredith, J. C., & Grover, M. A. (2020). Small Data Machine Learning: Classification and Prediction of Poly(ethylene terephthalate) Stabilizers Using Molecular Descriptors. ACS Applied Polymer Materials. https://doi.org/10.1021/acsapm.0c00921

Li, J., Sun, X., Xu, J., Tan, H., Zeng, E. Y., & Chen, D. (2020). Transplacental Transfer of Environmental Chemicals: Roles of Molecular Descriptors and Placental Transporters. Environmental Science and Technology. https://doi.org/10.1021/acs.est.0c06778

Sun, A., Ashammakhi, N., & Dokmeci, M. R. (2020). Methacrylate coatings for titanium surfaces to optimize biocompatibility. Micromachines, 11(1). https://doi.org/10.3390/mi11010087

Sabbah, D. A., Haroon, R. A., Bardaweel, S. K., Hajjo, R., & Sweidan, K. (2020). N-phenyl-6-chloro-4-hydroxy-2-quinolone-3-carboxamides: Molecular Docking, Synthesis, and Biological Investigation as Anticancer Agents. Molecules, 26(1), 73. https://doi.org/10.3390/molecules26010073

Wang, S., Kind, T., Tantillo, D. J., & Fiehn, O. (2020). Predicting in silico electron ionization mass spectra using quantum chemistry. Journal of Cheminformatics, 12(1), 1–11. https://doi.org/10.1186/s13321-020-00470-3

Yalamanchi, K. K., Monge-Palacios, M., van Oudenhoven, V. C. O., Gao, X., & Sarathy, S. M. (2020). Data Science Approach to Estimate Enthalpy of Formation of Cyclic Hydrocarbons. The Journal of Physical Chemistry A. https://doi.org/10.1021/acs.jpca.0c02785

Meshref, S., Li, Y., & Feng, Y. L. (2020). Prediction of liquid chromatographic retention time using quantitative structure-retention relationships to assist non-targeted identification of unknown metabolites of phthalates in human urine with high-resolution mass spectrometry. Journal of Chromatography A, 1634. https://doi.org/10.1016/j.chroma.2020.461691

Sharma, A., Kumar, R., Semwal, R., Aier, I., Tyagi, P., & Varadwaj, P. (2020). DeepOlf: Deep neural network based architecture for predicting odorants and their interacting Olfactory Receptors. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 1–1. https://doi.org/10.1109/tcbb.2020.3002154

Rybińska-Fryca, A., Sosnowska, A., & Puzyn, T. (2020). Representation of the structure-A key point of building QSAR/QSPR models for ionic liquids. Materials, 13(11), 1–11. https://doi.org/10.3390/ma13112500

Nedyalkova, M., & Simeonov, V. (2020). Multivariate chemometrics as a strategy to predict the allergenic nature of food proteins. Symmetry, 12(10), 1–19. https://doi.org/10.3390/sym12101616

Cui, X., Yang, R., Li, S., Liu, J., Wu, Q., & Li, X. (2020). Modeling and insights into molecular basis of low molecular weight respiratory sensitizers. Molecular Diversity, 0123456789. https://doi.org/10.1007/s11030-020-10069-3

Bonini, P., Kind, T., Tsugawa, H., Barupal, D. K., & Fiehn, O. (2020). Retip: Retention Time Prediction for Compound Annotation in Untargeted Metabolomics. Analytical Chemistry, 92(11), 7515–7522. https://doi.org/10.1021/acs.analchem.9b05765

Martinez-Mayorga, K., Madariaga-Mazon, A., Medina-Franco, J. L., & Maggiora, G. (2020). The impact of chemoinformatics on drug discovery in the pharmaceutical industry. Expert Opinion on Drug Discovery, 15(3), 293–306. https://doi.org/10.1080/17460441.2020.1696307

Alsenan, S. A., Al-Turaiki, I., & Hafez, A. (2020). Chemoinformatics for Data Scientists. Proceedings of the 22nd International Conference on Information Integration and Web-Based Applications & Services, 456–461. https://doi.org/10.1145/3428757.3429147

2019

Halder, A. K., & Cordeiro, M. N. D. S. (2019). Development of multi-target chemometric models for the inhibition of class I PI3K enzyme isoforms: A case study using QSAR-Co tool. International Journal of Molecular Sciences, 20(17). https://doi.org/10.3390/ijms20174191

Ghosh, D., Tetko, I., Klebl, B., Nussbaumer, P., & Koch, U. (2019). Analysis and Modelling of False Positives in GPCR Assays. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11731, pp. 764–770). Springer International Publishing. https://doi.org/10.1007/978-3-030-30493-5_71

Feiler, C., Mei, D., Vaghefinazari, B., Würger, T., Meißner, R. H., Luthringer-Feyerabend, B. J. C., Winkler, D. A., Zheludkevich, M. L., & Lamaka, S. V. (2019). In silico Screening of Modulators of Magnesium Dissolution. Corrosion Science, September, 108245. https://doi.org/10.1016/j.corsci.2019.108245

Tetko, I. V., Karpov, P., Bruno, E., Kimber, T. B., & Godin, G. (2019). Augmentation Is What You Need! Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11731, 831–835. https://doi.org/10.1007/978-3-030-30493-5_79