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


Mauri, A., & Bertola, M. (2024). AlvaBuilder: A Software for De Novo Molecular Design. Journal of Chemical Information and Modeling, 64(7), 2136–2142.

Prado-Romero, D. L., Saldívar-González, F. I., López-Mata, I., Laurel-García, P. A., Durán-Vargas, A., García-Hernández, E., … Medina-Franco, J. L. (2024). De Novo Design of Inhibitors of DNA Methyltransferase 1: A Critical Comparison of Ligand- and Structure-Based Approaches. Biomolecules, 14(7), 775.

Shah, S. K., Chaple, D. D., Masand, V. H., Jawarkar, R. D., Chaudhari, S., Abiramasundari, A., … Al-Hussain, S. A. (2024). Multi-Target In-Silico modeling strategies to discover novel angiotensin converting enzyme and neprilysin dual inhibitors. Scientific Reports, 14(1), 15991.

de Cripan, S. M., Arora, T., Olomí, A., Canela, N., Siuzdak, G., & Domingo-Almenara, X. (2024). Predicting the Predicted: A Comparison of Machine Learning-Based Collision Cross-Section Prediction Models for Small Molecules. Analytical Chemistry, 96(22), 9088–9096.

de Sousa, N. F., Duarte, G. D., Moraes, C. B., Barbosa, C. G., Martin, H., Muratov, N. N., … Scotti, M. T. (2024). In Silico and In Vitro Studies of Terpenes from the Fabaceae Family Using the Phenotypic Screening Model against the SARS-CoV-2 Virus. Pharmaceutics, 16(7), 912.

Bandini, E., Castellano Ontiveros, R., Kajtazi, A., Eghbali, H., & Lynen, F. (2024). Physicochemical modelling of the retention mechanism of temperature-responsive polymeric columns for HPLC through machine learning algorithms. Journal of Cheminformatics, 16(1), 72.

Gao, H., Li, S., Lan, Z., Pan, D., Naidu, G. S., Peer, D., … Santos, H. A. (2024). Comparative optimization of polysaccharide-based nanoformulations for cardiac RNAi therapy. Nature Communications, 15(1), 5398.

Khan, A. U., Porta, G. M., Riva, M., & Guadagnini, A. (2024). In-silico mechanistic analysis of adsorption of Iodinated Contrast Media agents on graphene surface. Ecotoxicology and Environmental Safety, 280(January), 116506.

Ullah, A., Shaheryar, M., & Lim, H. (2024). Machine Learning Approach for the Estimation of Henry’s Law Constant Based on Molecular Descriptors. Atmosphere, 15(6), 706.

Ait Lahcen, N., Liman, W., Oubahmane, M., Hdoufane, I., Habibi, Y., Alanazi, A. S., … Cherqaoui, D. (2024). Drug design of new anti-EBOV inhibitors: QSAR, homology modeling, molecular docking and molecular dynamics studies. Arabian Journal of Chemistry, 17(9), 105870.

Daghighi, A., Casanola-Martin, G. M., Iduoku, K., Kusic, H., González-Díaz, H., & Rasulev, B. (2024). Multi-Endpoint Acute Toxicity Assessment of Organic Compounds Using Large-Scale Machine Learning Modeling. Environmental Science & Technology, 58(23), 10116–10127.

Acuña-Guzman, V., Montoya-Alfaro, M. E., Negrón-Ballarte, L. P., & Solis-Calero, C. (2024). A Machine Learning Approach for Predicting Caco-2 Cell Permeability in Natural Products from the Biodiversity in Peru. Pharmaceuticals, 17(6), 750.

Pandey, S. K., & Roy, K. (2024). Predicting the performance and stability parameters of energetic materials (EMs) using a machine learning-based q-RASPR approach. Energy Advances.

Balraadjsing, S., J.G.M. Peijnenburg, W., & Vijver, M. G. (2024). Building species trait-specific nano-QSARs: Model stacking, navigating model uncertainties and limitations, and the effect of dataset size. Environment International, 188(May), 108764.

Das, S., Samal, A., Kumar, A., Ghosh, V., Kar, S., & Ojha, P. K. (2024). Comprehensive ecotoxicological assessment of pesticides on multiple avian species: Employing quantitative structure-toxicity relationship (QSTR) modeling and read-across. Process Safety and Environmental Protection, 188(May), 39–52.

Erickson, M., Casañola-Martin, G., Han, Y., Rasulev, B., & Kilin, D. (2024). Relationships between the Photodegradation Reaction Rate and Structural Properties of Polymer Systems. The Journal of Physical Chemistry B, 128(9), 2190–2200.

Obradović, D., Stavrianidi, A., Fedorova, E., Bogojević, A., Shpigun, O., Buryak, A., & Lazović, S. (2024). A comparative study of the predictive performance of different descriptor calculation tools: Molecular-based elution order modeling and interpretation of retention mechanism for isomeric compounds from METLIN database. Journal of Chromatography A, 1719(February), 464731.

Niu, H., Zhang, Y., Jia, Q., Wang, Q., & Yan, F. (2024). Property estimation of organic compounds based on QSPR models with norm indices. Chemical Engineering Science, 288(February), 119835.

Nakatani, K., Izumi, Y., Umakoshi, H., Yokomoto-Umakoshi, M., Nakaji, T., Kaneko, H., … Bamba, T. (2024). Wide-scope targeted analysis of bioactive lipids in human plasma by LC/MS/MS. Journal of Lipid Research, 65(1), 100492.

Gutkin, E., Gusev, F., Gentile, F., Ban, F., Koby, S. B., Narangoda, C., … Kurnikova, M. G. (2024). In silico screening of LRRK2 WDR domain inhibitors using deep docking and free energy simulations. Chemical Science.

Bhattacharjee, A., Kar, S., & Ojha, P. K. (2024). Unveiling G-protein coupled receptor kinase-5 inhibitors for chronic degenerative diseases: Multilayered prioritization employing explainable machine learning-driven multi-class QSAR, ligand-based pharmacophore and free energy-inspired molecular simulatio. International Journal of Biological Macromolecules, 269(P1), 131784.

Vigna, V., Cova, T. F. G. G., Nunes, S. C. C., Pais, A. A. C. C., & Sicilia, E. (2024). Machine Learning-Based Prediction of Reduction Potentials for Pt IV Complexes. Journal of Chemical Information and Modeling, 64(9), 3733–3743.

Tran, T. T. Van, Tayara, H., & Chong, K. T. (2024). AMPred-CNN: Ames mutagenicity prediction model based on convolutional neural networks. Computers in Biology and Medicine, 176(May), 108560.

Ghosh, S., & Roy, K. (2024). Quantitative read-across structure-activity relationship (q-RASAR): A novel approach to estimate the subchronic oral safety (NOAEL) of diverse organic chemicals in rats. Toxicology, 505(May), 153824.

Kumar, V., Banerjee, A., & Roy, K. (2024). Innovative strategies for the quantitative modeling of blood–brain barrier (BBB) permeability: harnessing the power of machine learning-based q-RASAR approach. Molecular Systems Design & Engineering, (Ml).

Dhanalakshmi, M., Sruthi, D., Das, K., Iqbal, M., Mohanan, V. P., Dave, S., & Muthulakshmi Andal, N. (2024). Graph theoretical descriptors differentiate d-Mannose isomers in the principal component proposed feature space: A computational approach. Carbohydrate Research, 541(May), 109147.

Pang, W., Chen, M., & Qin, Y. (2024). Prediction of anticancer drug sensitivity using an interpretable model guided by deep learning. BMC Bioinformatics, 25(1), 182.

Kumar, V., Banerjee, A., & Roy, K. (2024). Breaking the Barriers: Machine-Learning-Based c-RASAR Approach for Accurate Blood–Brain Barrier Permeability Prediction. Journal of Chemical Information and Modeling.

Galvez-Llompart, M., Hierrezuelo, J., Blasco, M., Zanni, R., Galvez, J., de Vicente, A., Pérez-García, A., & Romero, D. (2024). Targeting bacterial growth in biofilm conditions: rational design of novel inhibitors to mitigate clinical and food contamination using QSAR. Journal of Enzyme Inhibition and Medicinal Chemistry, 39(1), 1–18.

Pore, S., Banerjee, A., & Roy, K. (2024). Application of machine learning‐based read‐across structure‐property relationship (RASPR) as a new tool for predictive modelling: Prediction of power conversion efficiency (PCE) for selected classes of organic dyes in dye‐sensitized solar cells (DSSCs). Molecular Informatics, 43(4), 1–21.

Das, S., Samal, A., & Ojha, P. K. (2024). Chemometrics-driven prediction and prioritization of diverse pesticides on chickens for addressing hazardous effects on public health. Journal of Hazardous Materials, 471(April), 134326.

Godlewska, K., Białk-Bielińska, A., Mazierski, P., Zdybel, S., Sosnowska, A., Górzyński, D., Puzyn, T., Zaleska-Medynska, A., Klimczuk, T., & Paszkiewicz, M. (2024). Assessment of the application of selected metal-organic frameworks as advanced sorbents in passive extraction used in the monitoring of contaminants of emerging concern in surface waters. Science of The Total Environment, 927(March), 172215.

Ghosh, S., Chhabria, M. T., & Roy, K. (2024). Chemometric modeling of pharmaceuticals for partitioning between sludge and aqueous phase during the wastewater treatment process. Environmental Science and Pollution Research, 0123456789.

Nakayama, Y., Morishita, S., Doi, H., Hirano, T., & Kaneko, H. (2024). Molecular Design of Novel Herbicide and Insecticide Seed Compounds with Machine Learning. ACS Omega.

Nedyalkova, M., Robeva, R., Romanova, J., Yovcheva, K., Lattuada, M., & Simeonov, V. (2024). In silico screening of potential agonists of a glucagon-like peptide-1 receptor among female sex hormone derivatives. Journal of Biomolecular Structure and Dynamics, 1–12.

Rojas, C., Sarmiento, N., Ayora, E., & Pis Diez, R. (2024). Computational prediction of retention times of veterinary antibiotics obtained by liquid chromatography‐mass spectrometry. Journal of the Science of Food and Agriculture, November 2023.

Zhu, T., Yu, Y., Chen, M., Zong, Z., & Tao, C. (2024). An innovative method for predicting oxidation reaction rate constants by extracting vital information of organic contaminants (OCs) based on diverse molecular representations. Journal of Environmental Chemical Engineering, 12(2), 112473.

Chatterjee, M., & Roy, K. (2024). Predictive binary mixture toxicity modeling of fluoroquinolones (FQs) and the projection of toxicity of hypothetical binary FQ mixtures: a combination of 2D-QSAR and machine-learning approaches. Environmental Science: Processes & Impacts, 26(1), 105–118.

Tirapelle, M., Chia, D. N., Duanmu, F., Besenhard, M. O., Mazzei, L., & Sorensen, E. (2024). In-silico method development and optimization of on-line comprehensive two-dimensional liquid chromatography via a shortcut model. Journal of Chromatography A, 1721(March), 464818.

Li, W., Wen, Y., Wang, K., Ding, Z., Wang, L., Chen, Q., … Zhao, H. (2024). Developing a machine learning model for accurate nucleoside hydrogels prediction based on descriptors. Nature Communications, 15(1), 2603.

Semenyuta, I., Kovalishyn, V., Hodyna, D., Startseva, Y., Rogalsky, S., & Metelytsia, L. (2024). New QSTR models to evaluation of imidazolium- and pyridinium-contained ionic liquids toxicity. Computational Toxicology, 30(January), 100309.

de Souza, T. A., Lins, F. S. V., da Silva Lins, J., Alves, A. F., Cibulski, S. P., Brito, T. de A. M., … Tavares, J. F. (2024). Asclepiadoideae subfamily (Apocynaceae): ethnopharmacology, biological activities and chemophenetics based on pregnane glycosides. Phytochemistry Reviews, 2.

Kumar, A., Ojha, P. K., & Roy, K. (2024). Chemometric modeling of the lowest observed effect level (LOEL) and no observed effect level (NOEL) for rat toxicity. Environmental Science: Advances.

Tao, T., Tao, C., & Zhu, T. (2024). Machine-Learning-Based Prediction of Plant Cuticle–Air Partition Coefficients for Organic Pollutants: Revealing Mechanisms from a Molecular Structure Perspective. Molecules, 29(6), 1381.

Schieferdecker, S., Rottach, F., & Vock, E. (2024). In Silico Prediction of Oral Acute Rodent Toxicity Using Consensus Machine Learning. Journal of Chemical Information and Modeling.

Mudlaff, M., Sosnowska, A., Gorb, L., Bulawska, N., Jagiello, K., & Puzyn, T. (2024). Environmental impact of PFAS: Filling data gaps using theoretical quantum chemistry and QSPR modeling. Environment International, 185(March), 108568.

Bhattacharjee, A., Kar, S., & Ojha, P. K. (2024). First report on chemometrics-driven multilayered lead prioritization in addressing oxysterol-mediated overexpression of G protein-coupled receptor 183. Molecular Diversity, 0123456789.

Erickson, M., Casañola-Martin, G., Han, Y., Rasulev, B., & Kilin, D. (2024). Relationships between the Photodegradation Reaction Rate and Structural Properties of Polymer Systems. The Journal of Physical Chemistry B, 128(9), 2190–2200.

Kumar, A., Kumar, V., Ojha, P. K., & Roy, K. (2024). Chronic aquatic toxicity assessment of diverse chemicals on Daphnia magna using QSAR and chemical read-across. Regulatory Toxicology and Pharmacology, 148(January), 105572.

Han, M., Liang, J., Jin, B., Wang, Z., Wu, W., & Arp, H. P. H. (2024). Machine learning coupled with causal inference to identify COVID-19 related chemicals that pose a high concern to drinking water. IScience, 27(2), 109012.

Song, Z., Chen, J., Cheng, J., Chen, G., & Qi, Z. (2024). Computer-Aided Molecular Design of Ionic Liquids as Advanced Process Media: A Review from Fundamentals to Applications. Chemical Reviews, 124(2), 248–317.

Zdybel, S., Sosnowska, A., Kowalska, D., Sommer, J., Conrady, B., Mester, P., Gromelski, M., & Puzyn, T. (2024). Hybrid Machine Learning and Experimental Studies of Antiviral Potential of Ionic Liquids against P100, MS2, and Phi6. Journal of Chemical Information and Modeling.

Feng, C., Lin, Y., Le, S., Ji, J., Chen, Y., Wang, G., … Lu, D. (2024). Suspect, Nontarget Screening, and Toxicity Prediction of Per- and Polyfluoroalkyl Substances in the Landfill Leachate. Environmental Science & Technology.

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.

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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.

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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.

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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).

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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