Computational methods in drug discovery

Scholarly Works

• Munir, F.; Hussain, K.; Zhao, G.; Zhang, J. Daidzein Derivatives as Potential Inhibitors of Mutant EGFR in Non-Small Cell Lung Cancer: A Computational Study Supported by Molecular Docking, DFT, Molecular Dynamics Simulations, and MM/PBSA Techniques. Computer Methods and Programs in Biomedicine 2026, 109679. doi:10.1016/j.cmpb.2026.109679
• Mohamed, F. A. A. A.; El-Sagheir, A. M. K. Artificial intelligence-assisted lead optimization in drug discovery: bridging computational advances and translational challenges. Medicinal chemistry research : an international journal for rapid communications on design and mechanisms of action of biologically active agents 2026. doi:10.1007/s00044-026-03616-4
• Sulaimon, R.; Selvaraj, G.; Chan, N. W. C.; Hu, A.; Peslherbe, G. H. Unveiling Novel Metalloprotease Inhibitors Targeting Botulinum Neurotoxin Through Structure-Based Virtual Screening of Drug Libraries and Molecular Dynamics Simulations. International journal of molecular sciences 2026, 27, 8081. doi:10.3390/ijms27188081
• Gadbail, H.; Rewatkar, R.; Jumde, N.; Manker, M.; Nagre, P.; Zade, S. Quantum computing in drug discovery. Frontiers in Drug Discovery 2026, 6. doi:10.3389/fddsv.2026.1815176
• Zhang, X.; Zhang, L.; Guo, T.; Du, W.; Wang, S.; Li, Y. Application of emerging technologies in the antiviral field. Antiviral research 2026, 255, 106529. doi:10.1016/j.antiviral.2026.106529
• Chaabane, W.; Madani, S.; Mokhnache, K.; Bouabdallah, R. C.; Bensouici, C.; Tabti, R.; Boudiaf, M.; Abdelouahed, L. Synthesis, characterization and in silico study of (E)-4‑bromo-2-(1-(2-phenylhydrazineylidene)ethyl)phenol for antioxidant activity and for the inhibition of enzymes. Journal of Molecular Structure 2026, 1381, 147470. doi:10.1016/j.molstruc.2026.147470
• Gross, S.; Koch, P.; Strasser, A. Synthesis and Pharmacological Characterization of Novel Tetrahydropyridine-Based Muscarinic M5 Receptor Antagonists to Investigate the Difference in the SARs of Muscarinic Receptor Subtypes Utilizing Induced Fit Docking Studies. Journal of Medicinal Chemistry 2026. doi:10.1021/acs.jmedchem.6c00587
• Nguyen, B. V.; Truong, V.; Duong Thi Hong, H.; Tran-Trung, K.; Nguyen-Vo, T.-H.; Nguyen, B. P. Dual-Attention Multimodal Framework for Molecular Property Prediction. Journal of chemical information and modeling 2026, 66, 9787–9802. doi:10.1021/acs.jcim.5c02663
• Chandra, S.; Xu, J.; Bang, S.; Guo, R.; Ji, R.-R. Virtual screening of GPR37L1 small-molecule modulator for neuropathic pain. Pain 2026, 167, 2087–2103. doi:10.1097/j.pain.0000000000004075
• Jovanović, M.; Djikic-Stojsic, T.; Stanković, B.; Popovic-Nikolic, M.; Nikolic, K. A Computational Framework for the Design and Development of Isoform Selective PI3Kα Inhibitors as Novel Anticancer Agents. Molecules (Basel, Switzerland) 2026, 31, 2782. doi:10.26434/chemrxiv-2025-5kh41
• Paiva, V. A.; Pires, D. E. V.; Bressan, G. C.; Izidoro, S. C.; Silveira, S. A. BENDER DB: a database of protein binding sites across neglected disease proteomes. BMC Bioinformatics 2026. doi:10.1101/2025.02.18.638856
• Dhir, R.; Ghosh, P.; Sharma, D.; Asati, V. AI-driven computational drug design: tools, workflow and challenges. RSC advances 2026, 16, 37397–37422. doi:10.1039/d6ra02374f
• Tran, A.; Li, Y.; Li, L. Understanding Interindividual Variability in Biotransformation and Renal Clearance to Improve Personalized Toxicokinetic Modeling in Chemical Assessments. Environmental science & technology 2026, 60, 21532–21544. doi:10.1021/acs.est.6c03600
• Lanrewaju, A. A.; Folami, A. M.; Sabiu, S.; Swalaha, F. M. Advancing Antiviral Design: Integrating Natural Products, Computation and Targeted Delivery. Chemical biology & drug design 2026, 108, e70365. doi:10.1111/cbdd.70365
• Rampogu, S.; Balasubramaniyam, T.; Yoon, C.-H.; Kim, Y.; Kubiak, J. Z.; Lee, K. W. Explainable Artificial Intelligence (XAI) and Molecular Modeling Techniques to Discover Putative HER2 Inhibitors. International journal of molecular sciences 2026, 27, 6504. doi:10.3390/ijms27146504
• Ariyanto, A.; Kusuma, W. A.; Muhaimin, M.; Kamarulzaman, E. E.; Fakih, T. M.; Muchtaridi, M. Integrated Phytochemical and Pharmacological Insights into Premna serratifolia for Drug Discovery: A Narrative Review. Plants (Basel, Switzerland) 2026, 15, 2176. doi:10.3390/plants15142176
• Afolabi, S. O.; Feoktistova, V. A.; Skorb, E. V.; Shityakov, S. Classical substructure search versus LLM-assisted keyword retrieval in SciFinder: A case study on GABAB1 ligand discovery using a phenibut scaffold. Computational biology and chemistry 2026, 124, 109245. doi:10.1016/j.compbiolchem.2026.109245
• Noor, F.; Qamar, M. T. U. Deep Learning Pipeline for Accelerating Virtual Screening in Drug Discovery. Methods in molecular biology (Clifton, N.J.) 2026, 3021, 337–368. doi:10.1007/978-1-0716-5190-2_18
• Lee, K.; Moldagulov, G.; Grzybowski, B. A. The Fragility of Bioactivity Prediction: Rigorous Dataset Splits Expose the Illusion of ML Accuracy. Chemistry (Weinheim an der Bergstrasse, Germany) 2026, 32, e71208. doi:10.1002/chem.71208
• Guaitoli, V.; Alvarez-Ginarte, Y. M.; Montero-Cabrera, L. A.; De la Nuez Veulens, A.; Leclerc, F. IGF-1 receptor ATP-competitive inhibitors for the treatment of Ewing sarcoma: a comprehensive virtual screening analysis for a dataset of molecules showing a wide range of chemical structures. In silico pharmacology 2026, 14, 160. doi:10.1007/s40203-026-00668-7

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