Citations

PyDescriptor and PyDescriptorC* citations:

[1] F.V. Ryzhkov, Y.E. Ryzhkova, M.N. Elinson, Machine learning: Python tools for studying biomolecules and drug design, Molecular Diversity, (2025) 1-36.

[2] F.V. Ryzhkov, Y.E. Ryzhkova, M.N. Elinson, Python tools for structural tasks in chemistry, Molecular diversity, 29 (2025) 3733-3752.

[3] V.H. Masand, M.K. Patil, S.A. Al‐Hussain, A. Samad, V. Rastija, R.D. Jawarkar, G.S. Masand, R.G. Gawali, M.E. Zaki, Analyzing Oxygen Atom Distribution in FDA‐Approved Drugs to Enhance Drug Discovery Strategies, Chemical biology & drug design, 105 (2025) e70060.

[4] V.H. Masand, S. Al-Hussain, G.S. Masand, A. Samad, R. Gawali, S. Jadhav, M.E. Zaki, e-QSAR (Explainable AI-QSAR), molecular docking, and ADMET analysis of structurally diverse GSK3-beta modulators to identify concealed modulatory features vindicated by X-ray, Computational Biology and Chemistry, 115 (2025) 108324.

[5] S. Karmakar, R. Saikia, A. Das, K. Pathak, P. Das, B. Bhuyan, T. Alqahtani, H. Al Shmrany, B. Dhara, A. Kumer, Design and Development of Xanthone Hybrid for Potent Anti‐Inflammatory Effects: Synthesis and Evaluation, Journal of Cellular and Molecular Medicine, 29 (2025) e70477.

[6] R.D. Jawarkar, S. Mali, P.K. Deshmukh, R.G. Ingle, S.A. Al-Hussain, A.A. Al-Mutairi, M.E. Zaki, Synergizing GA-XGBoost and QSAR modeling: Breaking down activity aliffs in HDAC1 inhibitors, Journal of Molecular Graphics and Modelling, 135 (2025) 108915.

[7] R.A. Hussien, F.A.S. Alasmary, V.H. Masand, A. Samad, R.D. Jawarkar, G.S. Masand, S.A. Al-Hussain, M.E. Zaki, Identification of pharmacophore synergism for optimization of estrogen receptor beta binders for hormone dependent forms of breast cancer, Chemical Physics Impact, 10 (2025) 100872.

[8] R. Bounaceur, F. Paes, R. Privat, J.-N. Jaubert, AI-powered prediction of critical properties and boiling points: a hybrid ensemble learning and QSPR approach, Journal of Cheminformatics, 17 (2025) 132.

[9] R. Bounaceur, F. Paes, R. Privat, J.-N. Jaubert, AI-powered prediction of critical properties and boiling points: a hybrid ensemble learning and QSPR approach, Journal of Cheminformatics, 17 (2025).

[10] R. Bounaceur, N. Barthélemy, N. Delort, O. Herbinet, F. Battin-Leclerc, A Multimodal Learning Model based on a QSPR approach for the estimation of RON, MON and CN, for any C, H, O hydrocarbons, Fuel, 381 (2025) 133438.

[11] E. Bandini, A. Kajtazi, R. Szucs, F. Lynen, The role and choice of molecular descriptors for predicting retention times in HPLC: A comprehensive review, TrAC Trends in Analytical Chemistry, (2025) 118207.

[12] F.M. Abay, H. Özcan, A.Ş. Yılmaz, Ö. Zaim, Naringenin-Based Oximes and Hydrazones: Synthesis, Molecular Docking with Bovine Serum Albumin and Drug-Likeness, Admet Profiling Studies, Celal Bayar University Journal of Science, 21 (2025) 66-74.

[13] M.E.A. Zaki, S.A. Al-Hussain, A.A. Al-Mutairi, A. Samad, V.H. Masand, R.G. Ingle, V.D. Rathod, N.M. Gaikwad, S. Rashid, P.N. Khatale, P.V. Burakale, R.D. Jawarkar, Application of in-silico drug discovery techniques to discover a novel hit for target-specific inhibition of SARS-CoV-2 Mpro's revealed allosteric binding with MAO-B receptor: A theoretical study to find a cure for post-covid neurological disorder, PLoS One, 19 (2024) e0286848.

[14] G. Uluçam, DFT Calculation, ADME/T and Molecular Docking Approach of Methyl 2-oxo-1, 2-dihydrofuro [3, 4-d] pyrimidine-3 (4H) carboxylate, Journal of the Mexican Chemical Society, 68 (2024) 527-544.

[15] D. Song, T. Tang, R. Wang, H. Liu, D. Xie, B. Zhao, Z. Dang, G. Lu, Enhancing compound confidence in suspect and non-target screening through machine learning-based retention time prediction, Environmental Pollution, 347 (2024) 123763.

[16] S. Shah, D. Chaple, V.H. Masand, M.E. Zaki, S.A. Al-Hussain, A. Shah, S. Arora, R. Jawarkar, M. Tauqeer, In silico study to recognize novel angiotensin-converting-enzyme-I inhibitors by 2D-QSAR and constraint-based molecular simulations, Journal of Biomolecular Structure and Dynamics, 42 (2024) 2211-2230.

[17] A. Priyanka, K. Anbarasu, Comparison of human FTO and mutant R322Q with various ligands for poly malformation syndrome treatment,  AIP Conference Proceedings, AIP Publishing LLC, 2024, pp. 020074.

[18] Priyanka, K. Anbarasu, In-silico analysis on human FTO and mutant S319F with various ligands for poly malformation syndrome,  AIP Conference Proceedings, AIP Publishing LLC, 2024, pp. 020075.

[19] S. Naseem, A. Oneto, S. Ullah, S. Fatima, S.N. Mali, R.D. Jawarkar, A. Khan, R.D. Alharthy, H. Kashtoh, A. Al-Harrasi, Synthesis, biological evaluation, and molecular modelling of substituted thiazolyl thiourea derivatives: A new class of prolyl oligopeptidase inhibitors, International Journal of Biological Macromolecules, 275 (2024) 133571.

[20] S. Naseem, S. Fatima, S. Ullah, A. Khan, S.N. Mali, R.D. Jawarkar, A. Syed, A.M. Elgorban, A. Al‐Harrasi, Z. Shafiq, Carbonylbis (hydrazine‐1‐carbothioamide) derivatives as a new class of α‐glucosidase inhibitors and their mechanistic insights via molecular docking and dynamic simulations, Archiv der Pharmazie, 357 (2024) 2300604.

[21] F.T. Mtetwa, Transferable User-Friendly Machine Learning for Normal Boiling Point Prediction, (2024).

[22] Y.V. Milchevskiy, G.I. Kravatskaya, Y.V. Kravatsky, AAindexNC: estimating the physicochemical properties of non-canonical amino acids, including those derived from the PDB and PDBeChem Databank, International Journal of Molecular Sciences, 25 (2024) 12555.

[23] V.H. Masand, S.A. Al-Hussain, A.Y. Alzahrani, A.A. Al-Mutairi, R.A. Hussien, A. Samad, M.E. Zaki, Estrogen receptor alpha binders for hormone-dependent forms of breast cancer: e-QSAR and molecular docking supported by X-ray resolved structures, ACS omega, 9 (2024) 16759-16774.

[24] V.H. Masand, S. Al-Hussain, A.Y. Alzahrani, N.N. El-Sayed, C.I. Yeo, Y.S. Tan, M.E. Zaki, Leveraging nitrogen occurrence in approved drugs to identify structural patterns, Expert Opinion on Drug Discovery, 19 (2024) 111-124.

[25] V.H. Masand, S. Al-Hussain, A.Y. Alzahrani, A.A. Al-Mutairi, A. sultan Alqahtani, A. Samad, G.S. Masand, M.E. Zaki, GA-XGBoost, an explainable AI technique, for analysis of thrombin inhibitory activity of diverse pool of molecules and supported by X-ray, Chemometrics and Intelligent Laboratory Systems, 253 (2024) 105197.

[26] V.H. Masand, S. Al-Hussain, A.Y. Alzahrani, A.A. Al-Mutairi, A. Sultan Alqahtani, A. Samad, A.M. Alafeefy, R.D. Jawarkar, M.E. Zaki, Unveiling dynamics of nitrogen content and selected nitrogen heterocycles in thrombin inhibitors: a ceteris paribus approach, Expert Opinion on Drug Discovery, 19 (2024) 991-1009.

[27] R.D. Jawarkar, M.E. Zaki, S.A. Al-Hussain, A. Samad, L.C. Ming, S. Rashid, G.M. Elossaily, S. Yadav, S. Mali, QSAR and docking based lead optimization of nitrogen heterocycles for enhanced prostaglandin EP2 receptor agonistic potency, Chemical Physics Impact, 8 (2024) 100484.

[28] R.D. Jawarkar, M.E. Zaki, S.A. Al-Hussain, A.Y.A. Alzahrani, L.C. Ming, A. Samad, R.G. Ingle, S.N. Mali, QSAR modelling to predict structural features of certain sulfonamide as Urokinase-type Plasminogen Activator inhibitors, Chemical Physics Impact, 8 (2024) 100544.

[29] R.D. Jawarkar, M.E. Zaki, S.A. Al-Hussain, A.A. Al-Mutairi, A. Samad, N. Mukerjee, A. Ghosh, V.H. Masand, L.C. Ming, S. Rashid, QSAR modeling approaches to identify a novel ACE2 inhibitor that selectively bind with the C and N terminals of the ectodomain, Journal of Biomolecular Structure and Dynamics, 42 (2024) 2550-2569.

[30] R.D. Jawarkar, M.E. Zaki, S.A. Al-Hussain, A.A. Al-Mutairi, A. Samad, V. Masand, V. Humane, S. Mali, A.Y.A. Alzahrani, S. Rashid, Mechanistic QSAR modeling derived virtual screening, drug repurposing, ADMET and in-vitro evaluation to identify anticancer lead as lysine-specific demethylase 5a inhibitor, Journal of Biomolecular Structure and Dynamics, (2024) 1-31.

[31] R.D. Jawarkar, P. Sharma, S. Jain, S.N. Mali, Theoretical and Experimental Investigations for Identification of Anaphylactic Lymphoma Kinase inhibitors, Physical Chemistry Research, 12 (2024) 591-620.

[32] R.D. Jawarkar, S.N. Mali, R.G. Ingle, S.A. Al-Hussain, A.A. Al-Mutairi, P. Deshmukh, M.E. Zaki, Cheminformatics approaches to predict the bioactivity and to discover the pharmacophoric traits crucial to block NF-κB, Chemical Physics Impact, 9 (2024) 100720.

[33] R.D. Jawarkar, A.N. Khan, D.R. Bhagat, P.N. Khatale, P.V. Burakale, S. Farooqui, S.N. Mali, Molecular docking, QSAR, and ADME studies of some pyrrolo [1, 2-a] benzimidazole-based quinones as novel topoisomerase 2 beta (TOP2β) inhibitors, Chemical Physics Impact, 8 (2024) 100479.

[34] R.D. Jawarkar, A. Khan, S.N. Mali, P.K. Deshmukh, R.G. Ingle, S.A. Al-Hussain, A.A. Al-Mutairi, M.E. Zaki, Cheminformatics-driven prediction of BACE-1 inhibitors: Affinity and molecular mechanism exploration, Chemical Physics Impact, 9 (2024) 100754.

[35] R.S. Freitas, X. Jiang, Descriptors-based machine-learning prediction of cetane number using quantitative structure–property relationship, Energy and AI, 17 (2024) 100385.

[36] R.D. Alharthy, S. Khalid, S. Fatima, S. Ullah, A. Khan, S.N. Mali, R.D. Jawarkar, S.S. Dhabarde, H. Kashtoh, P. Taslimi, Synthesis of the chromone‐thiosemicarbazone scaffold as promising α‐glucosidase inhibitors: An in vitro and in silico approach toward antidiabetic drug design, Archiv der Pharmazie, 357 (2024) 2400140.

[37] M.E. Zaki, S.A. Al-Hussain, A.A. Al-Mutairi, A. Samad, A. Ghosh, S. Chaudhari, P.N. Khatale, P. Ajmire, R.D. Jawarkar, In-silico studies to recognize repurposing therapeutics toward arginase-I inhibitors as a potential onco-immunomodulators, Frontiers in Pharmacology, 14 (2023) 1129997.

[38] A.Ş. Yılmaz, G. Uluçam, Novel N-benzyl-2-oxo-1, 2-dihydrofuro [3, 4-d] pyrimidine-3 (4H)-carboxamide as anticancer agent: Synthesis, drug-likeness, ADMET profile, DFT and molecular modelling against EGFR target, Heliyon, 9 (2023).

[39] S. Sar, S. Mitra, P. Panda, S.C. Mandal, N. Ghosh, A.K. Halder, M.N.D. Cordeiro, In silico modeling and structural analysis of soluble epoxide hydrolase inhibitors for enhanced therapeutic design, Molecules, 28 (2023) 6379.

[40] A.A. Panagiotopoulos, E. Konstantinou, S.A. Pirintsos, E. Castanas, M. Kampa, Mining the ZINC database of natural products for specific, testosterone-like, OXER1 antagonists, Steroids, 199 (2023) 109309.

[41] H. Özcan, Pyrrole‐Based Schiff‐Bases: Synthesis, Fluorescent Properties, Molecular Docking and in silico ADME/Tox Profiling Studies, ChemistrySelect, 8 (2023) e202204168.

[42] E. Öner, İ. Demirhan, E.B. Kurutaş, S. Yalin, Investigation of Various Antibiotics in the Structure of Sars Cov-2 Mpro by Molecular Docking and Molecular Dynamics Simulation Method, Eurasian Journal of Medical and Biological Sciences, 3 (2023) 87-100.

[43] S. Mitra, S. Nandi, A.K. Halder, M.N.D. Cordeiro, SMILES-Based Bioactivity Descriptors to Model the Anti-dengue Virus Activity: A Case Study,  QSPR/QSAR Analysis Using SMILES and Quasi-SMILES, Springer2023, pp. 117-136.

[44] M.A.A.M. Khan, A. Ahsan, M.A. Khan, J.M. Sanjana, S. Biswas, M.A. Saleh, D.R. Gupta, M.N. Hoque, T.I. Sakif, M.M. Rahman, In-silico prediction of highly promising natural fungicides against the destructive blast fungus Magnaporthe oryzae, Heliyon, 9 (2023).

[45] R.D. Jawarkar, M.E. Zaki, S.A. Al-Hussain, A.Y. Abdullah Alzahrani, L.C. Ming, A. Samad, S. Rashid, S. Mali, G.M. Elossaily, Mechanistic QSAR analysis to predict the binding affinity of diverse heterocycles as selective cannabinoid 2 receptor inhibitor, Journal of Taibah University for Science, 17 (2023) 2265104.

[46] C. Jadhav, M. Rathore, V. Masand, P. Bodkhe, Study of Pharmacophoric Consensus Pattern for Benzoic Acid Derivatives of 1, 3, 4-Thiadiazole As Antibacterial Agent, (2023).

[47] E.H. Houssein, M.E. Hosney, M.M. Emam, E.M. Younis, A.A. Ali, W.M. Mohamed, Soft computing techniques for biomedical data analysis: open issues and challenges, Artificial intelligence review, 56 (2023) 2599-2649.

[48] M.R. Fissa, Y. Lahiouel, L. Khaouane, S. Hanini, Development of QSPR-ANN models for the estimation of critical properties of pure hydrocarbons, Journal of Molecular Graphics and Modelling, 121 (2023) 108450.

[49] E.A. Eno, C.-R. Cheng, H. Louis, T.E. Gber, W. Emori, I.-a.T. Ita, T.O. Unimke, L. Ling, S.A. Adalikwu, E.C. Agwamba, Investigation on the molecular, electronic and spectroscopic properties of rosmarinic acid: an intuition from an experimental and computational perspective, Journal of Biomolecular Structure and Dynamics, 41 (2023) 10287-10301.

[50] M.E. Zaki, S.A. Al-Hussain, S.N.A. Bukhari, V.H. Masand, M.M. Rathore, S.D. Thakur, V.M. Patil, Exploring the Prominent and Concealed Inhibitory Features for Cytoplasmic Isoforms of Hsp90 Using QSAR Analysis, Pharmaceuticals, 15 (2022) 303.

[51] M.E. Zaki, S.A. Al-Hussain, A.A. Al-Mutairi, V.H. Masand, A. Samad, R.D. Jawarkar, Mechanistic analysis of chemically diverse bromodomain-4 inhibitors using balanced QSAR analysis and supported by X-ray resolved crystal structures, Pharmaceuticals, 15 (2022) 745.

[52] J. Xu, Evolving drug design methodology: from QSAR to AIDD, (2022).

[53] G. Uluçam, U. Bagcı, A.Ş. Yılmaz, B. Yentürk, Schiff-base ligands containing phenanthroline terminals: Synthesis, characterization, biological activities and molecular docking study, Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 279 (2022) 121429.

[54] S.K. Shah, D.R. Chaple, 2D-QSAR modeling of quinazolinone derivatives as angiotensin II type 1a receptor blockers, International Journal of Quantitative Structure-Property Relationships (IJQSPR), 7 (2022) 1-20.

[55] S. Shah, S. Arora, D. Chaple, P. Badne, S. Yende, S. Khonde, S. Deshmukh, 2D-QSAR modeling of chalcone analogues as angiotensin converting enzyme inhibitor, Biointerface Res Appl Chem, 13 (2022) 370.

[56] M. Rudrapal, Dietary Polyphenols in Human Diseases: Advances and Challenges in Drug Discovery, (2022).

[57] I. Padhy, A. Mahapatra, F. Dilnawaz, T. Sharma, Dietary polyphenols in drug discovery by drug repurposing and computational screening,  Dietary Polyphenols in Human Diseases, CRC Press2022, pp. 255-279.

[58] N. NEDELJKOVIC, M. Nikolic, A. STANKOVIC, N. Jeremic, D. Tomovic, A. BUKONJIC, G. RADIC, M. Mijajlovic, Virtual screening, drug-likeness analysis, and molecular docking study of potentialsevere acute respiratory syndrome coronavirus 2 main protease inhibitors, Turkish Journal of Chemistry, 46 (2022) 116-146.

[59] N. Mukerjee, A. Das, R.D. Jawarkar, S. Maitra, P. Das, M.A. Castrosanto, S. Paul, A. Samad, M.E. Zaki, S.A. Al-Hussain, Repurposing food molecules as a potential BACE1 inhibitor for Alzheimer’s disease, Frontiers in aging neuroscience, 14 (2022) 878276.

[60] J.L. Medina-Franco, N. Sánchez-Cruz, E. López-López, B.I. Díaz-Eufracio, Progress on open chemoinformatic tools for expanding and exploring the chemical space, Journal of Computer-Aided Molecular Design, 36 (2022) 341-354.

[61] V.H. Masand, S.A. Al-Hussain, M.M. Rathore, S.D. Thakur, S. Akasapu, A. Samad, A.A. Al-Mutairi, M.E. Zaki, Pharmacophore synergism in diverse scaffold clinches in aurora kinase B, International Journal of Molecular Sciences, 23 (2022) 14527.

[62] A.A. Ksenofontov, M.M. Lukanov, P.S. Bocharov, M.B. Berezin, I.V. Tetko, Deep neural network model for highly accurate prediction of BODIPYs absorption, Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 267 (2022) 120577.

[63] M.A. Khan, M.A.A.M. Khan, J.M. Sanjana, A. Ahsan, D.R. Gupta, M.N. Hoque, T. Islam, In silico molecular docking evaluation reveals high potencies of some natural antifungal metabolites on melanin biosynthesis and appressoria formation enzymes in Magnaporthe oryzae, (2022).

[64] M.A. Khan, M.A. Al Mamun Khan, A. Mahfuz, J.M. Sanjana, A. Ahsan, D.R. Gupta, M.N. Hoque, T. Islam, Highly potent natural fungicides identified in silico against the cereal killer fungus Magnaporthe oryzae, Scientific Reports, 12 (2022) 20232.

[65] R.D. Jawarkar, P. Sharma, N. Jain, A. Gandhi, N. Mukerjee, A.A. Al-Mutairi, M.E. Zaki, S.A. Al-Hussain, A. Samad, V.H. Masand, QSAR, molecular docking, MD simulation and MMGBSA calculations approaches to recognize concealed pharmacophoric features requisite for the optimization of ALK tyrosine kinase inhibitors as anticancer leads, Molecules, 27 (2022) 4951.

[66] R.D. Jawarkar, R.L. Bakal, N. Mukherjee, A. Ghosh, M.E. Zaki, S.A. Al-Hussain, A.A. Al-Mutairi, A. Samad, A. Gandhi, V.H. Masand, QSAR evaluations to unravel the structural features in lysine-specific histone demethylase 1A inhibitors for novel anticancer lead development supported by molecular docking, MD simulation and MMGBSA, Molecules, 27 (2022) 4758.

[67] D. Ghosh, U. Koch, K. Hadian, M. Sattler, I.V. Tetko, Highly accurate filters to flag frequent hitters in AlphaScreen assays by suggesting their mechanism, Molecular Informatics, 41 (2022) 2100151.

[68] A. Ghosh, N. Mukerjee, B. Sharma, A. Pant, Y. Kishore Mohanta, R.D. Jawarkar, R.L. Bakal, E.M. Terefe, G.E.-S. Batiha, G. Mostafa-Hedeab, Target specific inhibition of protein tyrosine kinase in conjunction with cancer and SARS-COV-2 by olive nutraceuticals, Frontiers in pharmacology, 12 (2022) 812565.

[69] M. Gholamhoseinnia, M. Asadollahi-Baboli, Ranked binding energies of residues and data fusion to identify the active and selective pyrimidine-based Janus kinases 3 (JAK3) inhibitors, SAR and QSAR in Environmental Research, 33 (2022) 23-34.

[70] E.A. Eno, F.A. Patrick-Inezi, H. Louis, T.E. Gber, T.O. Unimuke, E.C. Agwamba, O.J. Ikenyirimba, J.A. Akpanke, E. Oyoita, E.U. Ejiofor, Theoretical investigation and antineoplastic potential of Zn (II) and Pd (II) complexes of 6-methylpyridine-2-carbaldehyde-N (4)-ethylthiosemicarbazone, Chemical Physics Impact, 5 (2022) 100094.

[71] P.R. Duchowicz, D.O. Bennardi, J.F. Aranda, Estudio QSAR de la toxicidad aguda de pesticidas en la lombriz Eisenia Foetida, (2022).

[72] A.E. Comesana, T.T. Huntington, C.D. Scown, K.E. Niemeyer, V.H. Rapp, A systematic method for selecting molecular descriptors as features when training models for predicting physiochemical properties, Fuel, 321 (2022) 123836.

[73] A.E. Comesana, T.T. Huntington, C.D. Scown, A systematic method for selecting molecular descriptors as features when training, (2022).

[74] S.N.A. Bukhari, M.A. Elsherif, K. Junaid, H. Ejaz, P. Alam, A. Samad, R.D. Jawarkar, V.H. Masand, Perceiving the Concealed and Unreported Pharmacophoric Features of the 5-Hydroxytryptamine Receptor Using Balanced QSAR Analysis, Pharmaceuticals, 15 (2022) 834.

[75] R.L. Bakal, R.D. Jawarkar, J. Manwar, M.S. Jaiswal, A. Ghosh, A. Gandhi, M.E. Zaki, S. Al-Hussain, A. Samad, V.H. Masand, Identification of potent aldose reductase inhibitors as antidiabetic (Anti-hyperglycemic) agents using QSAR based virtual Screening, molecular Docking, MD simulation and MMGBSA approaches, Saudi Pharmaceutical Journal, 30 (2022) 693-710.

[76] О.В. Тиньков, В.Ю. Григорьев, Л.Д. Григорьева, Прогнозирование времени биотрансформации органических соединений на примере авермектинов, Вестник Московского университета. Серия 2. Химия, 62 (2021) 313-332.

[77] M.E. Zaki, S.A. Al-Hussain, V.H. Masand, M.K. Sabnani, A. Samad, Mechanistic and Predictive QSAR Analysis of Diverse Molecules to Capture Salient and Hidden Pharmacophores for Anti-Thrombotic Activity, International Journal of Molecular Sciences, 22 (2021) 8352.

[78] M.E. Zaki, S.A. Al-Hussain, V.H. Masand, S. Akasapu, I. Lewaa, QSAR and pharmacophore modeling of nitrogen heterocycles as potent human N-myristoyltransferase (Hs-NMT) inhibitors, Molecules, 26 (2021) 1834.

[79] M.E. Zaki, S.A. Al-Hussain, V.H. Masand, S. Akasapu, S.O. Bajaj, N.N. El-Sayed, A. Ghosh, I. Lewaa, Identification of anti-SARS-CoV-2 compounds from food using QSAR-based virtual screening, molecular docking, and molecular dynamics simulation analysis, Pharmaceuticals, 14 (2021) 357.

[80] L. Wang, V. Upadhyay, C.D. Maranas, dGPredictor: Automated fragmentation method for metabolic reaction free energy prediction and de novo pathway design, PLoS computational biology, 17 (2021) e1009448.

[81] O.V. Tinkov, V.Y. Grigorev, L.D. Grigoreva, QSAR analysis of the acute toxicity of avermectins towards Tetrahymena pyriformis, SAR QSAR Environ Res, 32 (2021) 541-571.

[82] O. Tinkov, V. Grigorev, L.D. Grigoreva, Prediction of an Organic Compound’s Biotransformation Time: A Study Using Avermectins, Moscow University Chemistry Bulletin, 76 (2021) 231-247.

[83] O. Tinkov, V. Grigorev, L.D. Grigoreva, QSAR analysis of the acute toxicity of avermectins towards Tetrahymena pyriformis, SAR and QSAR in Environmental Research, 32 (2021) 541-571.

[84] S. Sosnin, M. Fedorov, Exploration of chemical space by machine learning, 2021.

[85] A.A. Panagiotopoulos, K. Kalyvianaki, G. Notas, S.A. Pirintsos, E. Castanas, M. Kampa, New antagonists of the membrane androgen receptor OXER1 from the ZINC natural product database, ACS omega, 6 (2021) 29664-29674.

[86] W.M. Mohamed, M. Hassaballah, Essam H. Houssein, Mosa E. Hosney, Diego Oliva, No Ortega-Sánchez, Metaheuristics in Machine Learning: Theory and Applications, 967 (2021) 337.

[87] V.H. Masand, M.E. Zaki, S.A. Al-Hussain, A.B. Ghorbal, S. Akasapu, I. Lewaa, A. Ghosh, R.D. Jawarkar, Identification of concealed structural alerts using QSTR modeling for Pseudokirchneriella subcapitata, Aquatic Toxicology, 239 (2021) 105962.

[88] V.H. Masand, M.K. Patil, N.N.E. El-Sayed, M.E. Zaki, Z. Almarhoon, S.A. Al-Hussain, Balanced QSAR analysis to identify the structural requirements of ABBV-075 (Mivebresib) analogues as bromodomain and extraterminal domain (BET) family bromodomain inhibitor, Journal of Molecular Structure, 1229 (2021) 129597.

[89] R. Jawarkar, R. Bakal, P. Khatale, I. Lewaa, C.M. Jain, J.V. Manwar, M.S. Jaiswal, QSAR, pharmacophore modeling and molecular docking studies to identify structural alerts for some nitrogen heterocycles as dual inhibitor of telomerase reverse transcriptase and human telomeric G-quadruplex DNA, Future Journal of Pharmaceutical Sciences, 7 (2021) 231.

[90] E.H. Houssein, N. Neggaz, M.E. Hosney, W.M. Mohamed, M. Hassaballah, Enhanced Harris hawks optimization with genetic operators for selection chemical descriptors and compounds activities, Neural computing and applications, 33 (2021) 13601-13618.

[91] E.H. Houssein, M.E. Hosney, D. Oliva, N. Ortega-Sánchez, W.M. Mohamed, M. Hassaballah, Drug Design and Discovery: Theory, Applications, Open Issues and Challenges,  Metaheuristics in Machine Learning: Theory and Applications, Springer2021, pp. 337-358.

[92] A. Gandhi, V. Masand, M.E. Zaki, S.A. Al-Hussain, A.B. Ghorbal, A. Chapolikar, Quantitative Structure–Activity Relationship Evaluation of MDA-MB-231 Cell Anti-Proliferative Leads, Molecules, 26 (2021) 4795.

[93] A. Gandhi, V. Masand, M. Zaki, S. Al-Hussain, A.B. Ghorbal, A. Chapolikar, QSAR analysis of sodium glucose co–transporter 2 (SGLT2) inhibitors for anti-hyperglycaemic lead development, SAR and QSAR in Environmental Research, 32 (2021) 731-744.

[94] C. Feng, Q. Xu, X. Qiu, Y.e. Jin, J. Ji, Y. Lin, S. Le, J. She, D. Lu, G. Wang, Evaluation and application of machine learning-based retention time prediction for suspect screening of pesticides and pesticide transformation products in LC-HRMS, Chemosphere, 271 (2021) 129447.

[95] P.R. Duchowicz, D.O. Bennardi, E.d.V. Ortiz, N.C. Comelli, QSAR models for insecticidal properties of plant essential oils on the housefly (Musca domestica L.), SAR and QSAR in Environmental Research, 32 (2021) 395-410.

[96] A.L. Bierling, I. Croy, T. Hummel, G. Cuniberti, A. Croy, Olfactory perception in relation to the physicochemical odor space, Brain Sciences, 11 (2021) 563.

[97] N. Bayerisches, Modelling False Positives in High Throughput Assays, Universität München, 2021.

[98] О.В. Тиньков, В.В. Люленова, В.Ю. Григорьев, РАЗРАБОТКА ГИБРИДНЫХ ПРОТИВООПУХОЛЕВЫХ ЛЕКАРСТВЕННЫХ СРЕДСТВ, Вестник Приднестровского университета. Серия: Медико-биологические и химические науки, (2020) 77-85.

[99] О. Тиньков, В. Григорьев, Л. Григорьева, QSPR анализ способности индивидуальных авермектинов к биоконцентрированию, Химическая безопасность, 4 (2020) 8-23.

[100] A.P. Toropova, P.R. Duchowicz, L.M. Saavedra, E.A. Castro, A.A. Toropov, The use of the index of ideality of correlation to build up models for bioconcentration factor, Molecular Informatics, 39 (2020) 1900070.

[101] A.P. Toropova, P.R. Duchowicz, L.M. Saavedra, E.A. Castro, A.A. Toropov, The Use of the Index of Ideality of Correlation to Build Up Models for Bioconcentration Factor, Mol Inform, 39 (2020) e1900070.

[102] Y. Tao, W. Zou, S. Nanayakkara, E. Kraka, PyVibMS: a PyMOL plugin for visualizing vibrations in molecules and solids, Journal of Molecular Modeling, 26 (2020) 290.

[103] N.A. Szewczuk, P.R. Duchowicz, A.B. Pomilio, QSAR analysis for the inhibition of the mutagenic activity by anthocyanin derivatives, International Journal of Quantitative Structure-Property Relationships (IJQSPR), 5 (2020) 69-82.

[104] M. Moussaoui, M. Laidi, S. Hanini, M. Hentabli, Artificial neural network and support vector regression applied in quantitative structure-property relationship modelling of solubility of solid solutes in supercritical CO 2, Kemija u industriji: Časopis kemičara i kemijskih inženjera Hrvatske, 69 (2020) 611-630.

[105] D.D. Matyushin, A.K. Buryak, Gas chromatographic retention index prediction using multimodal machine learning, Ieee Access, 8 (2020) 223140-223155.

[106] V.H. Masand, V. Rastija, M.K. Patil, A. Gandhi, A. Chapolikar, Extending the identification of structural features responsible for anti-SARS-CoV activity of peptide-type compounds using QSAR modelling, SAR and QSAR in Environmental Research, 31 (2020) 643-654.

[107] V.H. Masand, S. Akasapu, A. Gandhi, V. Rastija, M.K. Patil, Structure features of peptide-type SARS-CoV main protease inhibitors: Quantitative structure activity relationship study, Chemometrics and intelligent laboratory systems, 206 (2020) 104172.

[108] S.J.Y. Macalino, J.B. Billones, V.G. Organo, M.C.O. Carrillo, In silico strategies in tuberculosis drug discovery, Molecules, 25 (2020) 665.

[109] E.H. Houssein, M.E. Hosney, D. Oliva, W.M. Mohamed, M. Hassaballah, A novel hybrid Harris hawks optimization and support vector machines for drug design and discovery, Computers & Chemical Engineering, 133 (2020) 106656.

[110] E.H. Houssein, M.E. Hosney, M. Elhoseny, D. Oliva, W.M. Mohamed, M. Hassaballah, Hybrid Harris hawks optimization with cuckoo search for drug design and discovery in chemoinformatics, Scientific Reports, 10 (2020) 14439.

[111] A.K. Halder, M.N. Dias Soeiro Cordeiro, Advanced in silico methods for the development of anti-leishmaniasis and anti-trypanosomiasis agents, Current Medicinal Chemistry, 27 (2020) 697-718.

[112] C.R. García‐Jacas, Y. Marrero‐Ponce, R. Vivas‐Reyes, J. Suárez‐Lezcano, F. Martinez‐Rios, J.E. Terán, L. Aguilera‐Mendoza, Distributed and multicore QuBiLS‐MIDAS software v2. 0: computing chiral, fuzzy, weighted and truncated geometrical molecular descriptors based on tensor algebra, Journal of Computational Chemistry, 41 (2020) 1209-1227.

[113] P.R. Duchowicz, D.O. Bennardi, E.V. Ortiz, N.C. Comelli, QSAR models for the fumigant activity prediction of essential oils, Journal of Molecular Graphics and Modelling, 101 (2020) 107751.

[114] P.R. Duchowicz, QSPR studies on water solubility, octanol-water partition coefficient and vapour pressure of pesticides, SAR and QSAR in Environmental Research, 31 (2020) 135-148.

[115] О.В. Тиньков, П.Г. Полищук, Д.С. Хачатрян, А.В. Колотаев, А.Н. Балаев, В.Н. Осипов, В.Ю. Григорьев, Количественный анализ «структура–противоопухолевая активность» и рациональный молекулярный дизайн бифункциональных VEGFR-2/HDAC-ингибиторов, Компьютерные исследования и моделирование, 11 (2019) 911-930.

[116] O. Tinkov, P. Polisciuc, D. Khachatryan, A. Kolotaev, A. Balaev, V. Osipov, V. Grigorev, Quantitative analysis of" structure-Anticancer activity" and rational molecular design of bi-functional VEGFR-2/HDAC-inhibitors, Computer research and modeling, 11 (2019) 911-930.

[117] S.B. Suryawanshi, R.T. Parihar, K.N. Puri, V.H. Masand, Consensus Pharmacophore Modeling Analysis for Human Cellular Cytotoxicity (Hepg2) Activity of 2-Anilino 4-Amino Substituted Quinazolines, Journal of Current Pharma Research, 9 (2019) 2727-2733.

[118] V.H. Masand, N.N. Elsayed, S.D. Thakur, N. Gawhale, M.M. Rathore, Quinoxalinones based aldose reductase inhibitors: 2D and 3D‐QSAR analysis, Molecular Informatics, 38 (2019) 1800149.

[119] V.H. Masand, N.N. El-Sayed, V. Rastija, M.M. Rathore, M. Karnaš, Identification of prodigious and under-privileged structural features for RG7834 analogs as Hepatitis B virus expression inhibitor, Medicinal chemistry research, 28 (2019) 2270-2278.

[120] V.H. Masand, N.N. El-Sayed, M.U. Bambole, V.R. Patil, S.D. Thakur, Multiple quantitative structure-activity relationships (QSARs) analysis for orally active trypanocidal N-myristoyltransferase inhibitors, Journal of Molecular Structure, 1175 (2019) 481-487.

[121] S.L. Kumbhare, S.B. Suryawanshi, V.H. Masand, S.B. Borul, Consensus Pharmacophore identification for antimycobacterial DprE1 inhibitory activity of substituted hydantoins, Journal of Current Pharma Research, 9 (2019) 2721-2726.

[122] M. Karnaš, Vijay H. Masand, Nahed NE El-Sayed, Vesna Rastija, Mithilesh M. Rathore &, Med Chem Res, 28 (2019) 2270-2278.

[123] M.R. Fissa, Y. Lahiouel, L. Khaouane, S. Hanini, QSPR estimation models of normal boiling point and relative liquid density of pure hydrocarbons using MLR and MLP-ANN methods, Journal of Molecular Graphics and Modelling, 87 (2019) 109-120.

[124] S.B. Borul, S.L. Kumbhareb, Development of Pharmacophore Modelfor Antipsychotic Action of N-Substituted 2-Phenylcyclopropylmethylamines, (2019).

[125] K. Xiao, M. Chen, T. Zhao, Q. Zhang, Computer aided compound identification based on a highly selective topological index, Chemometrics and Intelligent Laboratory Systems, 178 (2018) 56-64.

[126] A.A. Toropov, A.P. Toropova, A. Roncaglioni, E. Benfenati, Prediction of biochemical endpoints by the coral software: Prejudices, paradoxes, and Results,  Computational Toxicology: Methods and Protocols, Springer2018, pp. 573-583.

[127] S.D. Thakur, S.B. Suryawanshi, V.H. Masand, G.H. Kurhade, In Silico Analysis for Anti-Malarial Activity of 2-Anilino 4-Amino Substituted Quinazolines, (2018).

[128] S. Sosnin, D. Karlov, I.V. Tetko, M.V. Fedorov, Comparative study of multitask toxicity modeling on a broad chemical space, Journal of chemical information and modeling, 59 (2018) 1062-1072.

[129] V.H. Masand, N.N. El-Sayed, M.U. Bambole, S.A. Quazi, Multiple QSAR models, pharmacophore pattern and molecular docking analysis for anticancer activity of α, β-unsaturated carbonyl-based compounds, oxime and oxime ether analogues, Journal of Molecular Structure, 1157 (2018) 89-96.

[130] T.B. Kimber, S. Engelke, I.V. Tetko, E. Bruno, G. Godin, Synergy effect between convolutional neural networks and the multiplicity of SMILES for improvement of molecular prediction, arXiv preprint arXiv:1812.04439, (2018).

[131] Q. Jiang, J. Ma, A novel graph kernel on chemical compound classification, Journal of bioinformatics and computational biology, 16 (2018) 1850026.

[132] P.R. Duchowicz, Linear regression QSAR models for polo-like kinase-1 inhibitors, Cells, 7 (2018) 13.

[133] R.S. Jisha, L. Aswathy, V.H. Masand, J.M. Gajbhiye, I.G. Shibi, Exploration of 3, 6-dihydroimidazo (4, 5-d) pyrrolo (2, 3-b) pyridin-2 (1H)-one derivatives as JAK inhibitors using various in silico techniques, In Silico Pharmacology, 5 (2017) 9.

[134] J.S. Waghmare, P.G. Zanwar, A Pharmacophoric Pattern for 6‑Nitro-2, 3-Dihydroimidazo [2, 1‑B][1, 3] Oxazoles for Leishmania Infantum.