Application advances of deep learning methods for de novo drug design and molecular dynamics simulation

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ColdstartCPI: Induced-fit theory-guided DTI predictive model with improved generalization performance

Zhao, Q., Zhao, H., Guo, L., Zheng, K., Li, Y., Ling, Q., Tang, J. P., Li, Y., & Wang, J. (2025) · Nature Communications · doi:10.1038/s41467-025-61745-7 · Cited by 21 · see citing works

Molecular dynamics simulations of proteins: an in-depth review of computational strategies, structural insights, and their role in medicinal chemistry and drug development

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Graph neural networks with molecular segmentation for property prediction and structure–property relationship discovery

Chen, Z., Li, D., Liu, M., & Liu, J. (2023) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2023.108403 · Cited by 17 · see citing works

Deep learning in computer-aided drug design: a case study

Ahmad, S., Aslam, D., Ansari, A., Bhat, A. M., & Raza, K. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-22299-3.00012-8 · Cited by 17 · see citing works

Immune subtype identification and multi-layer perceptron classifier construction for breast cancer

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Insights of potential trypanocidal effect of the synthetic derivative (2E)-1-(4-aminophenyl)-3-(2,4-dichlorophenyl)prop-2-en-1-one: in vitro assay, MEV analysis, quantum study, molecular docking, molecular dynamics, MPO analysis, and predictive ADMET

Marinho, M. M., da Rocha, M. N., Magalhães, E. P., Ribeiro, L. R., Roberto, C. H. A., Almeida-Neto, F. W. Q., Monteiro, M. L., Nunes, J. V. S., de Menezes, R. R. P. P. B., Marinho, E. S., et al. (2024) · Naunyn-Schmiedeberg s Archives of Pharmacology · doi:10.1007/s00210-024-03138-z · Cited by 16 · see citing works

Combining diffusion and transformer models for enhanced promoter synthesis and strength prediction in deep learning

Lei, X., Wang, X., Chen, G., Liang, C., Li, Q., Jiang, H., & Xiong, W. (2025) · mSystems · doi:10.1128/msystems.00183-25 · Cited by 15 · see citing works

An approach combining deep learning and molecule docking for drug discovery of cathepsin L

Li, Q., Wang, H., Yang, W., & Yang, J. (2023) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2023.2174522 · Cited by 15 · see citing works

Drug-target binding affinity prediction method based on a deep graph neural network

Ma, D., Li, S., & Chen, Z. (2022) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2023012 · Cited by 14 · see citing works

Full-life-cycle eco-friendly polymeric insulating materials: research progress and future prospects

Li, W., Zhang, Y., Zhao, X., Liu, R., Liu, H., Huang, Z., & Zhang, G. (2023) · Journal of Physics D Applied Physics · doi:10.1088/1361-6463/acdc36 · Cited by 14 · see citing works

Lead generation of UPPS inhibitors targeting MRSA: Using 3D-QSAR pharmacophore modeling, virtual screening, molecular docking, and molecular dynamic simulations

Qandeel, B. M., Mowafy, S., Abouzid, K. A. M., & Farag, N. A. (2024) · BMC Chemistry · doi:10.1186/s13065-023-01110-1 · Cited by 14 · see citing works

Artificial Intelligence in Drug Discovery: A Bibliometric Analysis and Literature Review

He, B., Guo, J., Tong, H. H. Y., & To, W. M. (2024) · Mini-Reviews in Medicinal Chemistry · doi:10.2174/0113895575271267231123160503 · Cited by 16 · see citing works

Systematic Improvement of the Performance of Machine Learning Scoring Functions by Incorporating Features of Protein-Bound Water Molecules

Qu, X., Dong, L., Zhang, J., Si, Y., & Wang, B. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c00916 · Cited by 14 · see citing works

AI's role in pharmaceuticals: Assisting drug design from protein interactions to drug development

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Exploring structure-based drug discovery of GPCRs beyond the orthosteric binding site

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Ring Opening of Substituted 3-[2-(4-Nitrobenzoyl)hydrazinylidene]furan-2(3H)-ones with Primary Alcohols. Analgesic Activity and Acute Toxicity of the Products

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Keras/TensorFlow in Drug Design for Immunity Disorders

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Elucidation of critical pH-dependent structural changes in Botulinum Neurotoxin E

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Recent progress in artificial intelligence and machine learning for novel diabetes mellitus medications development

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AΙ-Driven Drug Repurposing: Applications and Challenges

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Deep generative molecular design and its value in modern drug discovery

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Exploring the Anti‐Cancer Potential of Hispidin: A Comprehensive in Silico and in Vitro Study on Human Osteosarcoma Saos2 Cells

Benarous, K., Serseg, T., Mermer, A., Tahmasebifar, A., Boulebd, H., & Linani, A. (2024) · Chemistry & Biodiversity · doi:10.1002/cbdv.202301833 · Cited by 10 · see citing works

From roots to codes: Applications of computer-aided drug discovery from medicinal plants

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Molecular Dynamics Simulations of Structurally Nanoengineered Antimicrobial Peptide Polymers Interacting with Bacterial Cell Membranes

Jayawardena, A., Hung, A., Qiao, G. G., & Hajizadeh, E. (2024) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.4c06691 · Cited by 10 · see citing works

Discovering virtual antiperovskites as solid-state electrolytes through active learning

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Synergistic Application of Docking and Machine Learning for Improved Binding Pose

Li, Y., Lin, H., He, Y., Yuan, Y., Zou, R., Zhou, G., Zhang, L., & Zheng, H. (2024) · National Science Open · doi:10.1360/nso/20230058 · Cited by 9 · see citing works

Unveiling Microscopic Mechanisms of Chemical Mechanical Polishing via Multi‐Scale Theoretical Calculations

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Identification of promising anti-EBOV inhibitors:de novodrug design, molecular docking and molecular dynamics studies

Mohamed, E. A. R., Abdelwahab, S. F., Alqaisi, A. M., Nasr, A. M. S., & Hassan, H. A. (2022) · Royal Society Open Science · doi:10.1098/rsos.220369 · Cited by 8 · see citing works

Learning with uncertainty to accelerate the discovery of histone lysine-specific demethylase 1A (KDM1A/LSD1) inhibitors

Wang, D., Wu, Z., Shen, C., Bao, L., Luo, H., Wang, Z., Yao, H., De, K., Luo, C., & Hou, T. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac592 · Cited by 9 · see citing works

Synthesis and Hemostatic Activity of Substituted Potassium 1-Cyano-3-{(3-cyano-4,5,6,7-tetrahydrobenzo[b]thiophen-2-yl)amino}-5-oxopenta-1,3-dien-2-olates

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AI-Driven Transfer Learning and Generative Model (TransGenGRU) Enables the Drug Discovery of Novel Natural Guaianolide Sesquiterpene Derivatives as Potent NLRP3 Inhibitors

Lin, W., Wang, P., Zhang, Y., Lü, W., Yang, M., Zhu, Z., Yin, X., Lv, J., Zheng, Z., Mei, S., et al. (2025) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.5c01663 · Cited by 8 · see citing works

In Silico Strategies for Cancer Model Development and Anticancer Drug Testing

Shyam, P. (2025) · Springer eBooks · doi:10.1007/978-981-97-5742-8_7 · Cited by 7 · see citing works

From molecular nanoarchitectonics to device integration: Coordination chemistry in next-generation photonic, electronic, and mechanical technologies

Khan, S. U., Khan, M. U., Alissa, M., Alghamdi, A. A., Alghamdi, S. A., Alshehri, M. A., Aloraini, G. S., Albelasi, A., Alshammari, M. S., & Alnafesah, G. M. (2025) · Coordination Chemistry Reviews · doi:10.1016/j.ccr.2025.217187 · Cited by 7 · see citing works

Bridging traditional and contemporary approaches in computational medicinal chemistry: opportunities for innovation in drug discovery

de Oliveira, A. S. (2025) · RSC Medicinal Chemistry · doi:10.1039/d5md00700c · Cited by 7 · see citing works

AI Deep Learning Generative Models for Drug Discovery

Bai, Q., Ma, J., & Xu, T. (2024) · Springer eBooks · doi:10.1007/978-3-031-46238-2_23 · Cited by 7 · see citing works

How did we get there? AI applications to biological networks and sequences

Anteghini, M., Gualdi, F., & Oliva, B. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110064 · Cited by 9 · see citing works

Enhanced compound-protein binding affinity prediction by representing protein multimodal information via a coevolutionary strategy

Guo, B., Zheng, H., Jiang, H., Li, X., Guan, N., Zuo, Y., Zhang, Y., Yang, H., & Wang, X. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbac628 · Cited by 7 · see citing works

Recent advances in computational studies on voltage‐gated sodium channels: Drug design and mechanism studies

Wang, G., Xu, L., Chen, H., Liu, Y., Pan, P., & Hou, T. (2022) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1641 · Cited by 7 · see citing works

Impact of quantum and neuromorphic computing on biomolecular simulations: Current status and perspectives

Diaz-Pier, S., & Carloni, P. (2024) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2024.102817 · Cited by 6 · see citing works

Predicting Protein–Ligand Binding Affinity Using Fusion Model of Spatial-Temporal Graph Neural Network and 3D Structure-Based Complex Graph

Li, G., Yuan, Y., & Zhang, R. (2024) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-024-00644-9 · Cited by 6 · see citing works

DTiGNN: Learning drug-target embedding from a heterogeneous biological network based on a two-level attention-based graph neural network

Muniyappan, S., Rayan, A. X. A., & Varrieth, G. T. (2023) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2023419 · Cited by 6 · see citing works

A Survey on Evolutionary Computation-Based Drug Discovery

Yu, Q., Lin, Q., Ji, J., Zhou, W., He, S., Zhu, Z., & Tan, K. C. (2024) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2024.3382145 · Cited by 6 · see citing works

Deep learning: A game changer in drug design and development

Das, S. K., Mishra, R., Samanta, A., Shil, D., & Roy, S. D. (2025) · Advances in pharmacology · doi:10.1016/bs.apha.2025.01.008 · Cited by 5 · see citing works

Cheminformatics-based screening and evaluation of phytochemicals as CDK2 inhibitors in colorectal cancer therapy

Tabassum, N., Hossan, M. S., Islam, M. M., Hasan, M., Islam, M. S., Masud, S., Ahmed, F., & Siddiquee, N. H. (2025) · PLoS ONE · doi:10.1371/journal.pone.0331438 · Cited by 6 · see citing works

Artificial Intelligence in drug discovery and development: current landscape, challenges, and future perspectives

Seyhan, A. A., & Carini, C. (2026) · Critical Reviews in Oncology/Hematology · doi:10.1016/j.critrevonc.2026.105461 · Cited by 6 · see citing works

Curcumin interferes with chitin synthesis in Aedes aegypti: a computational and experimental investigation

Rao, P., Ninama, J., Dudhat, M., Goswami, D., & Rawal, R. (2023) · Molecular Diversity · doi:10.1007/s11030-023-10672-0 · Cited by 5 · see citing works

Current Stage and Future Perspectives for Homology Modeling, Molecular Dynamics Simulations, Machine Learning with Molecular Dynamics, and Quantum Computing for Intrinsically Disordered Proteins and Proteins with Intrinsically Disordered Regions

Coskuner‐Weber, O., & Uversky, V. N. (2024) · Current Protein and Peptide Science · doi:10.2174/0113892037281184231123111223 · Cited by 6 · see citing works

Interaction of Twenty-Seven Bicyclo Derivatives with VEGF Receptors as a Cancer Treatment Alternative

Maria, L., Figueroa-Valverde, L., Marcela, R., Alvarez-Ramirez, M., Mateu-Armand, V., & CauichCarrillo, R. (2024) · Archive of International Journal of Cancer and Allied Science · doi:10.51847/m9nooahmol · Cited by 5 · see citing works

Explaining structure-activity relationships using locally faithful surrogate modelsPreprint

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PROTACs in targeted protein degradation: Advances in development and AI-enhanced drug discovery

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Efficient Computational Framework for Target-Specific Active Peptide Discovery: A Case Study on IL-17C Targeting Cyclic Peptides

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DReAmocracy: A Method to Capitalise on Prior Drug Discovery Efforts to Highlight Candidate Drugs for Repurposing

Savva, K., Zachariou, M., Bourdakou, M. M., Dietis, N., & Spyrou, G. M. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25105319 · Cited by 4 · see citing works

De novo Molecular Design using Deep Learning

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CSstep: Step-by-step exploration of the chemical space of drug molecules via multi-agent and multi-stage reinforcement learning

Che, X., Zhao, Y., Liu, Q., Yu, F., Gao, H., & Zhang, L. (2025) · Chemical Engineering Science · doi:10.1016/j.ces.2025.122048 · Cited by 3 · see citing works

Flow Modification Effects and Mechanisms of Silicone Powder and PEG on UHMWPE/HDPE Blends: Insights from Experimental and Molecular Dynamics Simulations

Wang, G., Liu, J., Qin, S., Zhang, H., & Zhou, H. (2025) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.5c02737 · Cited by 3 · see citing works

Exploring the potential of artificial intelligence and machine learning in healthcare: challenges and research directions

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A beginner’s approach to deep learning applied to VS and MD techniques

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Chapter 9. Machine Learning in the Area of Molecular Dynamics Simulations

Wang, S., & Riniker, S. (2020) · RSC drug discovery/RSC drug discovery series · doi:10.1039/9781788016841-00184 · Cited by 4 · see citing works

De novo drug design framework based on mathematical programming method and deep learning model

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Deep learning-based molecular dynamics simulation for structure-based drug design against SARS-CoV-2

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Designed with interactome-based deep learning

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