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


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.

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.

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.

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.

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).

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

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.

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.

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.

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.

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.

Tao, L., Chen, G., & Li, Y. (2021). Machine learning discovery of high-temperature polymers. Patterns, 2(4), 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.

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.

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.

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.

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.

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.

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.

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.

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