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


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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.


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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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.

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.

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.

Pandey, S. K.; Banerjee, A.; Roy, K. Machine Learning-Based q-RASPR Predictions of Detonation Heat for Nitrogen-Containing Compounds. Mater. Adv. 2023.

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.

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.

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.

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.

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.

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.

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).

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.

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.

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.

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.

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.

Liu, S., & Kosugi, Y. (2023). Human Brain Penetration Prediction Using Scaling Approach from Animal Machine Learning Models. AAPS Journal, 25(5), 1–10.

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.

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.

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.

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.

Kartowikromo, K. Y.; Olajide, O. E.; Hamid, A. M. Collision Cross Section Measurement and Prediction Methods in Omics. J. Mass Spectrom. 2023, No. April.

Elsayad, A. M., Zeghid, M., Ahmed, H. Y., & Elsayad, K. A. (2023). Exploration of Biodegradable Substances Using Machine Learning Techniques. Sustainability, 15(17), 12764.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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).

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.

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.

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.

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.

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.

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.

Kumar, A., Ojha, P. K., & Roy, K. (2023). QSAR modeling of chronic rat toxicity of diverse organic chemicals. Computational Toxicology, 26(February), 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.

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.

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.

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.

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.

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).

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.


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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Zushi, Y. (2022). Direct Prediction of Physicochemical Properties and Toxicities of Chemicals from Analytical Descriptors by GC–MS. Analytical Chemistry, 94(25), 9149–9157.

Cox, P. B., & Gupta, R. (2022). Contemporary Computational Applications and Tools in Drug Discovery. ACS Medicinal Chemistry Letters, 13(7), 1016–1029.

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.

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.

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.

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.

Nath, A., & Roy, K. (2022). Chemometric modeling of acute toxicity of diverse aromatic compounds against Rana japonica. Toxicology in Vitro, 83(June), 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.


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).

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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.

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,

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Morishita, T., & Kaneko, H. (2022). Development of Prediction Models for the Self-Accelerating Decomposition Temperature of Organic Peroxides. ACS Omega, 7(2), 2429–2437.

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.

Schmidt, S., Schindler, M., & Eriksson, L. (2022). Block‐wise exploration of molecular descriptors with Multi‐block Orthogonal Component Analysis (MOCA). Molecular Informatics, 2100165, 2100165.

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.

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.

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.

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.

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.

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.

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).


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.

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.

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.

Saçan, M.T., Önlü, S. and Tugcu, G. (2021). Chemometric Modeling of Algal Toxicity. In Chemometrics and Cheminformatics in Aquatic Toxicology, K. Roy (Ed.).

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Alsenan, S., Al-Turaiki, I., & Hafez, A. (2021). A deep learning approach to predict blood-brain barrier permeability. PeerJ Computer Science, 7, e515.

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.

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