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Robust enzyme discovery and engineering with deep learning using CataPro

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Alzheimer’s Disease: Exploring Pathophysiological Hypotheses and the Role of Machine Learning in Drug Discovery

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Predicting Inhibition of CDK2 with SAnDReS: The Application of Machine Learning to Navigate the Scoring Function Space

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Bioactivity Deep Learning for Complex Structure-Free Compound-Protein Interaction Prediction

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Fully Flexible Molecular Alignment Enables Accurate Ligand Structure Modeling

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Continuous Engineering of Phenylalanine Ammonia Lyase from Lettuce (Lactuca sativa L.) for Efficient Synthesis of 3,4-Substituted Phenylalanine

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Could statistical potential models achieve comparable or better performance than deep learning models?

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An efficient deep learning-based strategy to screen inhibitors for GluN1/GluN3A receptor

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AdptDilatedGCN: Protein-ligand binding affinity prediction based on multi-scale interaction fusion mechanism and dilated GCN

Wang, W., Ma, L., Liu, D., Zhang, H., Wu, G., & Zhou, Y. (2025) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2025.143751 · Cited by 3 · see citing works

Computer-Aided Drug Design Across Breast Cancer Subtypes: Methods, Applications and Translational Outlook

Tian, W. X., Hu, Y., Gao, X., Yang, J., & Jiang, W. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms262110744 · Cited by 2 · see citing works

DTAP: a unified graph transformer framework for joint prediction of drug–target affinity and docking pose

Liu, J., Ding, Y., Yan, Y., Zheng, L., & Pan, Y. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag069 · Cited by 1 · see citing works

Advances in computational prediction of RNA-small molecule binding affinity

Arulsamy, S., Arora, P., & Kumar, S. (2026) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-026-00781-x · Cited by 1 · see citing works

Robustness of Protein–Ligand Binding Affinity Prediction Models to Docked and Predicted Structures

Eaves, J. N., & Woldring, D. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6c00592 · Cited by 1 · see citing works

Critical Artifacts Improve Reproducibility of Protein–Ligand Binding Affinity Prediction Models on CASF-2016

Eaves, J. N., Needs, A. A., & Woldring, D. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6c01192 · Cited by 1 · see citing works

Bioinformatics-Driven Structural and Pharmacological Analysis of SLITRK1 in Tourette Syndrome: Impact of S656M Mutation Using Molecular Dynamics, Docking, and Reinforcement Learning

Aktaş, E., İslim, A., Kırboğa, K. K., Yildiz, D., Özgentürk, N. Ö., Rudrapal, M., Khan, J., Achar, R. R., Silina, E. V., Manturova, N. E., et al. (2025) · Computation · doi:10.3390/computation13020029

Protective Effects of Honey-derived Antioxidants Against Bisphenol A-induced Inhibition of Testosterone Synthesis: An In Silico Molecular Docking Study

Bhattacharjee, S., Debbarma, S., Chakraborty, S., Chakraborty, T. R., & Sharma, A. (2025) · International Journal of Infertility & Fetal Medicine · doi:10.5005/jp-journals-10016-1380

AI strategies for drug discovery and bioactivity prediction: opportunities and challenges

Azad, A. K. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-34059-8.00013-0

DUW-MGCA: A Dynamic Uncertainty-Weighted Multi-Granularity Coattention Framework for Protein–Ligand Interaction Prediction from Hybrid QM/MD Data

Tan, B., Niu, Y., & He, Z. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c03023

From undruggable to degradable: A deep learning-enabled framework for precision orthopaedic protein degradation

Zhang, L., Yan, W., Wang, D., & Ma, J. (2026) · Journal of Orthopaedic Translation · doi:10.1016/j.jot.2026.101118

Graph Neural Networks for Kinase–Inhibitor Affinity Prediction Using Docking Scores and Binding-Site Features

Turner, M., Nguyen, S., Clark, D., & Wilson, E. (2024) · Pharmacophore · doi:10.51847/vepyukpdnr

Integrating multimodal features with deep learning for protein solubility prediction

Wang, Z., Tan, L. H., Zheng, L., Rajapakse, J. C., & Mu, Y. (2026) · Journal of Cheminformatics · doi:10.1186/s13321-026-01225-2

HMS-PLI: A multi-task deep learning framework for protein-ligand interaction prediction with hierarchical multi-scale encoding

Mursalim, M. K. N., Susanty, M., Syafarina, I., & Syafiandini, A. F. (2026) · In Silico Research in Biomedicine · doi:10.1016/j.insi.2026.100502

AlphaDTA: integrating AlphaFold3 embeddings and 3D complex structures for drug–target binding affinity prediction

Chung, M. L., Park, S., & Lee, H. (2026) · Journal of Cheminformatics · doi:10.1186/s13321-026-01259-6

AI-Driven Molecular Dynamics Simulation for Target Protein Binding Mechanism Analysis

Li, Y., Jiao, Y., & Zhang, Y. (2026) · International Symposium on Bioinformatics and Computational Biology (BCB) · doi:10.1145/3820664.3820676

A Unified Hierarchical Multiscale Fusion Framework for Drug–Target Affinity Prediction: From Benchmark Performance to Nanomolar Inhibitor Discovery

Liu, S., Zhang, X., Feng, H., Li, Y., Gong, X., Liang, Y., Yao, X., & Liu, H. (2026) · Advanced Science · doi:10.1002/advs.77345

OmniScore: A geometric foundation encoder for universal biomolecular representation and scoringPreprint

Bui, T.-C., 洪怡喻, Song, J., Tao, Y., Chung, I., Park, J. G., Ko, J., & Lee, J. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.08.28.747942

UniPoseScore: A Unified Graphormer3D-Based Framework for Protein–Ligand Binding Pose Scoring and Refinement

Zhang, J., Ren, X., Peng, Z., & Wei, H. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6c00858

Integrative machine learning approaches for enzyme kinetic parameter prediction

Zhou, R., Xi, W., Yin, P., Wang, S., Zhang, Y., & Wei, Y. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag533

Deep Learning-Assisted Prediction of Protein–Ligand Interactions for Accelerated Drug Discovery

P.Dineshkumar (2026) · Frontiers in Life Sciences Research

Advancing Drug Discovery through a Deep Learning-Based Protein-Ligand Interaction Prediction Tool

Hunge, S. S., Yadao, A. R., Shewate, V. T., Lalsare, P. N., Prasannan, S., & Ramulu, K. P. (2025) · International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE) · doi:10.1109/iitcee64140.2025.10915324 · Cited by 1 · see citing works

DeepLPI: a novel deep learning-based model for protein–ligand interaction prediction for drug repurposing

Wei, B., Zhang, Y., & Gong, X. (2022) · Scientific Reports · doi:10.1038/s41598-022-23014-1 · Cited by 53 · see citing works

Interformer: An Interaction-Aware Model for Protein-Ligand Docking and Affinity PredictionPreprint

Lai, H., Wang, L., Qian, R., Ye, G., Huang, J., Wu, F., Wu, F., Zeng, X., & Liu, W. (2024) · Research Square · doi:10.21203/rs.3.rs-3995849/v1

PREDICTIVE MODELING OF PROTEIN-LIGAND INTERACTIONS USING GRAPH-NEURAL NETWORKS: A BIOINFORMATICS APPROACH

Prassanna, S., & Mallick, D. (2026) · Iterative International Publishers eBooks · doi:10.58532/nbennurv6b3158c1