Citations - Alvascience

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

2022

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

2021

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

2020

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

2019

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

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

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

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