arXiv Machine Learning June 15, 2026 By Shuai Li, Chuan-Xian Ren, Yuhao Li, Ziqi Huang, Yue Pan, Mingzhe Tang, Hong Yan

Curvature-Guided Geometric Representation for Protein-Ligand Binding Affinity Prediction

Read the original on arXiv Machine Learning →

arXiv:2606. 14159v1 Announce Type: new Abstract: Protein-ligand binding affinity (PLA) prediction is critical in drug discovery.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jun 15

Curvature-Informed Potential Energy Surface for Protein-Ligand Binding Affinity Prediction

arXiv:2606. 14217v1 Announce Type: new Abstract: Accurate prediction of protein-ligand binding affinity is essential for structure-based drug discovery.

By Peng-Fei Sun, Chuan-Xian Ren, Hong Yan

benchmarks safety

arXiv Machine Learning

2d ago

READ: A Retrieval-Alignment Diffusion Framework for Structure-based Drug Design

arXiv:2506. 14488v2 Announce Type: replace-cross Abstract: Structure-based drug design (SBDD) models are central to modern pharmaceutical research, enabling the rational exploration of protein-ligand interactions at atomic resolution.

By Dong Xu, Zhangfan Yang, Junchuang Cai, Sisi Yuan, Zexuan Zhu, Jianqiang Li, Junkai Ji

diffusion benchmarks safety

arXiv AI

Jul 24

Beyond SBDD: Geometric Deep Learning in Polypharmacology and Multi-target Drug Design

arXiv:2607. 20550v1 Announce Type: cross Abstract: The traditional "one drug, one target" paradigm of structure-based drug design (SBDD) frequently proves inadequate for treating multifactorial diseases such as cancer and neurodegenerative disorders, owing to compensatory signaling pathways and the emergence of drug resistance.

By Tianming Han, Zhijie Pan, Wenchi Ge, Qi Zhao

rag diffusion reinforcement-learning multimodal benchmarks

arXiv Machine Learning

Jul 22

DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models

arXiv:2607. 19237v1 Announce Type: new Abstract: Designing small molecule ligands that bind with high affinity to specific protein pockets is a fundamental goal in drug discovery, as small molecules constitute a major fraction of approved therapeutics.

By Yiming Qin, Kai Yi, Miruna Cretu, Sjors H. W. Scheres, Pietro Li\`o, Pascal Frossard

safety

arXiv Machine Learning

Jun 5

An accurate nucleic acid-small molecule docking framework via geometric deep learning with large-scale pretraining

arXiv:2606. 05198v1 Announce Type: cross Abstract: Nucleic acids are increasingly recognized as therapeutic targets beyond conventional protein-centered drug discovery, yet accurate and efficient docking of small molecules to nucleic acid structures remains challenging.

By Shi Li (College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, P. R. China), Xujun Zhang (College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, P. R. China), Mingquan Liu (Faculty of Health Sciences, University of Macau, Macau SAR, China), Hui Zhang (College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, P. R. China, Shanghai Innovation Institute, Shanghai, China), Shuoying Jia (College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, P. R. China, Shanghai Innovation Institute, Shanghai, China), Yu Kang (College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, P. R. China, Shanghai Innovation Institute, Shanghai, China), Tingjun Hou (College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, P. R. China, Zhejiang Provincial Key Laboratory for Intelligent Drug Discovery and Development, Jinhua Institute of Zhejiang University, Zhejiang, China), Peichen Pan (College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, P. R. China, Zhejiang Provincial Key Laboratory for Intelligent Drug Discovery and Development, Jinhua Institute of Zhejiang University, Zhejiang, China)

fine-tuning benchmarks

arXiv Machine Learning

Jun 18

KEPLA: A Knowledge-Enhanced Deep Learning Framework for Accurate Protein-Ligand Binding Affinity Prediction

arXiv:2506. 13196v5 Announce Type: replace Abstract: Accurate prediction of protein-ligand binding affinity is critical for drug discovery.

By Han Liu, Keyan Ding, Peilin Chen, Yinwei Wei, Liqiang Nie, Dapeng Wu, Shiqi Wang

rag benchmarks safety

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Curvature-Informed Potential Energy Surface for Protein-Ligand Binding Affinity Prediction

READ: A Retrieval-Alignment Diffusion Framework for Structure-based Drug Design

Beyond SBDD: Geometric Deep Learning in Polypharmacology and Multi-target Drug Design

DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models

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KEPLA: A Knowledge-Enhanced Deep Learning Framework for Accurate Protein-Ligand Binding Affinity Prediction