Computational methods in drug discovery

Scholarly Works

• 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 Informationand Modeling 2026. 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
• 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.
• 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 2026, 31, 2782. doi:10.3390/molecules31162782
• 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, 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
• Sağıroğlu, Ö.; Arıkan, M. Dual Activity Microbial Peptides Catalog. Scientific data 2026, 13. doi:10.1038/s41597-026-07521-8
• Mohammed, M. O.; Moronfolu, I. A.; Ola-Williams, I. O.; Adediran, R. I. GC–MS phytochemical profiling, antibacterial screening, and molecular docking analysis of the ethanolic extract of Cassia fistula stem-bark: an in-vitro and in-silico approach. Discover Chemistry 2026, 3. doi:10.1007/s44371-026-00766-7
• Ishfaq, A.; Tajammal, A.; Maira, R.; Qureshi, A. W.; Aleeza, B.; Basra, M. A. R. Antibacterial flavonoid derivatives: in silico ADMET, DFT, Molecular Docking, MD Simulation and in vitro verification. In silico pharmacology 2026, 14, 153. doi:10.1007/s40203-026-00644-1
• Rahbar, A.; Tabibian, S. S.; Madanchi, H.; Ahmad, S.; Naderian, R.; Hemmatian, N.; Hedayati, S. B.; Hatamian, M.; Sanami, S. Computational screening of natural compounds from traditional Persian medicinal plant for the finding of potential antidepressant. Scientific reports 2026, 16. doi:10.1038/s41598-026-53032-2
• Howard, J. B.; Narayanasamy, A.; Lindert, S. Improving Protein Structure Prediction Using Integrative Cryo-EM and Ion Mobility Mass Spectrometry Modeling. Journal of chemical theory and computation 2026, 22, 5909–5917. doi:10.1021/acs.jctc.6c00265
• Tuncal, A.; Kalkan, R. Integrative In Silico Identification of TP53-Associated Drug Repurposing Candidates in Lung Adenocarcinoma. Pharmaceuticals (Basel, Switzerland) 2026, 19, 761. doi:10.3390/ph19050761

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