arXiv Machine Learning

TriGlue: a Biology-Inspired Generative Model for Generating Molecular Glue-Induced Ternary Complex

arXiv:2607. 22143v1 Announce Type: new Abstract: Molecular glue degraders have emerged as a promising strategy for targeted protein degradation by inducing ternary complex formation between an E3 ubiquitin ligase and a target protein.

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 Machine Learning

Jun 24

Sesame: Structure-Aware Molecular Generation via Spatial Density-Map Conditioning

arXiv:2606. 23856v1 Announce Type: new Abstract: Generative molecular models for drug design are a promising direction with much active research.

By Konstantin Yatsenko, Arvind Thiagarajan

diffusion fine-tuning

arXiv Machine Learning

Jun 11

ProHiFlo: Hierarchical Flow Matching with Functional Guidance for De Novo Protein Generation

arXiv:2606. 11243v1 Announce Type: new Abstract: De novo protein generation has transformative potential in therapeutic design, enzyme engineering, and synthetic biology.

By Chuanzhen Wang, Meade Cleti, Pete Jano

diffusion

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 AI

Jun 2

Demystifying Multimodal Biomolecular Co-design With Intrinsic Geodesic Coupling

arXiv:2606. 01628v1 Announce Type: cross Abstract: Biomolecules such as proteins and small-molecule ligands play a central role in biological systems, arising from the tight interplay between sequence and three-dimensional structure.

By Keyue Qiu, Xintong Wang, Zhilong Zhang, Hao Zhou, Wei-Ying Ma

multimodal

arXiv AI

Jun 8

ShallowBench: Benchmarking Generative Drug Design Models on Shallow-Pocket Targets

arXiv:2606. 06717v1 Announce Type: cross Abstract: While generative AI models have demonstrated remarkable success in structure-based drug design, they predominantly rely on deep binding pockets and struggle to sample effective ligands for challenging low-pocketability targets, such as the historically "undruggable" oncology targets KRAS and MYC.

By Saket Reddy, Shiwei Liu

benchmarks

arXiv AI

1d ago

Protein Structure Prediction: From Evolutionary Constraints to Generative Modeling

arXiv:2608. 16094v1 Announce Type: new Abstract: Accurate protein structure prediction is fundamental to structural biology because protein structure underlies molecular function and provides a basis for mechanistic interpretation.

By Wengan He, Yongsheng Luo, Lihong Jiang, Wenhui Xu, Yu Li

safety

arXiv AI

Jul 21

Do Language Models Dream of Binding Molecules? Benchmarking LLMs under Spatial Constraints

arXiv:2607. 18144v1 Announce Type: cross Abstract: Structure-based drug design (SBDD) leverages the 3D structure of protein targets, often complemented by other spatial constraints, to generate candidate binding molecules.

By Thomas MacDougall, Maksim Kuznetsov, Roman Schutski, Rim Shayakhmetov, Maxim Malkov, Vladimir Aladinskiy, Alex Aliper, Alex Zhavoronkov

llms diffusion benchmarks

arXiv AI

Jun 3

From Holo Pockets to Electron Density: GPT-style Drug Design with Density

arXiv:2605. 08767v2 Announce Type: replace Abstract: Recent advances in generative modeling have enabled significant progress in structure-based drug design (SBDD).

By Jiahao Chen, Letian Gao, Yanhao Zhu, Wenbiao Zhou, Bing Su, Zhi John Lu, Bo Huang

llms safety

arXiv AI

Jun 24

JEDEL: Zero-Shot DNA-Encoded Library Design for Early-Stage Drug Discovery

arXiv:2606. 23745v1 Announce Type: cross Abstract: We present JEDEL, a framework for generating synthesis-ready DNA-encoded libraries (DELs) directly from three-dimensional pharmacophore representations of active ligands.

By Zygimantas Jocys, Zhanxing Zhu, Henriette M. G. Willems, Katayoun Farrahi

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 AI

Aug 12

Proteo-R1: Reasoning Foundation Models for De Novo Protein Design

arXiv:2605. 02937v2 Announce Type: replace-cross Abstract: Deep learning in de novo protein design has achieved atomic-level fidelity.

By Fang Wu, Weihao Xuan, Heli Qi, Hanqun Cao, Heng-Jui Chang, Zeqi Zhou, Haokai Zhao, Ma Jian, Carl Ma, Yu-Chi Cheng, Kuan Pang, Xiangru Tang, Zehong Wang, Guanlue Li, Hanchen Wang, Kejun Ying, Pan Lu, Chiho Im, Seungju Han, Peng Xia, Tinson Xu, Yinxi Li, Deyao Zhu, Pheng-Ann Heng, Naoto Yokoya, Masashi Sugiyama, Li Erran Li, Jure Leskovec, Yejin Choi

llms diffusion multimodal benchmarks safety

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