Molecular Docking

Latest papers

14 papers3 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

BridgeDock: Taming Generative Co-Folding Prior for Molecular Docking with Diffusion Bridge
Oct 31, 2026Shikun Feng, Zipeng Yang, Qiuyi Li +4Molecular Docking Protein Structure Prediction
Zhongguancun Academy, Beijing, China. · Zhongguancun Institute of Artificial Intelligence, Beijing, China. · School of Physical Science and Technology, Beijing University of Posts and Telecommunications, Beijing, China. +2
Curriculum Learning for Generative Flexible Docking
Oct 20, 2026Wenyin Zhou, Hossein AzizpourMolecular Docking Structure-Based Drug Design
KTH Royal Institute of Technology · Science for Life Laboratory
PMO-Dock: Benchmarking Docking, Specificity, and Generalization in Molecular Optimization
Oct 13, 2026Gor Simonyan, Tatevik Abrahamyan, Narek Abelyan +2Molecular Optimization Benchmark Design
Yerevan State University · University of Cambridge
Improving scoring functions for protein-protein docking with LambdaLoss
Sep 18, 2026Richard Zhu, Darren Xu, Lee-Shin Chu +1Molecular Docking Protein-Protein Interaction Prediction
Department of Statistics Harvard University · Department of Chemical and Biomolecular Engineering Johns Hopkins University
Scalable High-Fidelity Macromolecular Docking for GPU-Accelerated Supercomputers
Aug 7, 2026Xiangyu Meng, Peng Chen, Mingzhen Li +7Molecular Docking GPU Kernel Optimization
College of Computer Science and Technology, Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software, China University of Petroleum (East China), Qingdao, China · A*STAR Institute of Advanced Intelligence and Computing, Singapore · RIKEN Center for Computational Science, Kobe, Japan +2
Accurate structural modeling of chemically diverse molecular interfaces with Vilya-2
Jul 28, 2026Vilya Research, :, Pascal Sturmfels +10Molecular Docking Drug Discovery
Vilya Research
CORAL: Learning Amyloid Fibril Ligand Docking with Cooperative Binding Rewards
Jul 19, 2026Yasheng Sun, Bohan Li, Youqi Tao +1Molecular Docking Reward Design for RL
Center of Excellence for Generative AI, KAUST · MoE Key Lab of Artificial Intelligence, Shanghai Jiao Tong University · Stern Laboratory, Brigham and Women’s Hospital, Harvard University
Q-Score: A Quantum-Native Scoring Function for Molecular Docking
Jul 2, 2026Kangyu Zheng, Yidong Zhou, Ruihao Li +3Protein-Ligand Binding Affinity Prediction Molecular Docking
Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong SAR · Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ · Computational Life Sciences, Cleveland Clinic, Cleveland, OH +1
Harmonic Torsional Diffusion for Protein-Ligand Flexible Docking
Jun 18, 2026Maksim Zhdanov, Pavel Strashnov, Vladislav KurenkovMolecular Docking Diffusion Models
AXXX
An accurate nucleic acid-small molecule docking framework via geometric deep learning with large-scale pretraining
May 22, 2026Shi Li, Xujun Zhang, Mingquan Liu +5Molecular Docking Structure-Based Drug Design
College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, P. R. China · College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China · Faculty of Health Sciences, University of Macau, Macau SAR, China +4
Entropic Riemannian Neural Optimal Transport
May 5, 2026Alessandro Micheli, Silvia Sapora, Anthea Monod +1Molecular Docking Riemannian Geometry
Imperial College London London, UK · University of Oxford Oxford, UK · Statens Serum Institut Copenhagen, Denmark +1
Benchmarking Single-Pose Docking, Consensus Rescoring, and Supervised ML on the LIT-PCBA Library: A Critical Evaluation of DiffDock, AutoDock-GPU, GNINA, and DiffDock-NMDN
May 3, 2026Youssef Abo-Dahab, Xiaoiang Xiang, Joanne Chun +1Molecular Docking Learning to Rank
Department of Bioengineering and Therapeutic Sciences, Schools of Pharmacy and Medicine, University of California, San Francisco, San Francisco, CA, USA · UCSF–Stanford Center of Excellence in Regulatory Science and Innovation (CERSI), San Francisco, CA, USA
FuseDiff: Symmetry-Preserving Joint Diffusion for Dual-Target Structure-Based Drug Design
Mar 5, 2026Jianliang Wu, Anjie Qiao, Zhen Wang +2Structure-Based Drug Design Drug Discovery
School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China · School of Computer Science and Engineering, Guangdong Province Key Laboratory of Computational Science, Sun Yat-sen University, Guangzhou, China · Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China +1
Matcha: Multi-Stage Riemannian Flow Matching for Accurate and Physically Valid Molecular Docking
Oct 16, 2025Daria Frolova, Talgat Daulbaev, Egor Sevriugov +4Molecular Docking Structure-Based Drug Design
Ligand Pro, Moscow, Russia · Skolkovo Institute of Science and Technology, Artificial Intelligence Center, Moscow, Russia · Skolkovo Institute of Science and Technology, Center for Molecular and Cellular Biology, Moscow, Russia +1

Oct 31, 2026J/K move · Enter open · S save

BridgeDock: Taming Generative Co-Folding Prior for Molecular Docking with Diffusion Bridge

Shikun Feng, Zipeng Yang, Qiuyi Li, Mingze Yin+3

Zhongguancun Academy, Beijing, China. · Zhongguancun Institute of Artificial Intelligence, Beijing, China. · School of Physical Science and Technology, Beijing University of Posts and Telecommunications, Beijing, China.+2

Molecular Docking Protein Structure Prediction

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Molecular docking aims to predict the three-dimensional structure of a protein-ligand complex; in the conventional structure-based setting, it is conditioned on an input protein structure and ligand, often starting from an apo receptor in flexible docking or a holo receptor in rigid docking. Recent co-folding models exemplified by AlphaFold 3 achieve strong accuracy in protein-ligand complex prediction, but they typically generate complexes from sequence-derived inputs and therefore do not directly use apo structures as structural priors in docking, leading to a larger conformational search space and weaker robustness in difficult or few-step settings. We present BridgeDock, a framework that adapts pretrained co-folding backbones to flexible docking by modeling the apo-to-holo transition as a diffusion bridge, allowing generation from the initial protein-ligand state rather than from Gaussian noise. To make this bridge formulation compatible with pretrained denoisers, we introduce an alignment mechanism that maps bridge states to the original denoising schedule of the pretrained model. Experiments on standard flexible docking benchmarks show that BridgeDock consistently outperforms strong co-folding baselines. Moreover, BridgeDock is computationally efficient at inference time and remains effective even with very few denoising steps.