ArticleChemical science2024
DiffBindFR: an SE(3) equivariant network for flexible protein-ligand docking.
Article in Chemical science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Protein Spatial Structure Meets Artificial Intelligence: Revolutionizing Drug Synergy-Antagonism in Precision Medicine.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Pooled it
- Teaching diffusion models physics: reinforcement learning for physically valid diffusion-based docking.Chemical science · 2026Article
- Deep generative models for 3D structure-based drug design and molecular optimisation: a comprehensive survey.Journal of computer-aided molecular design · 2026Review
- Efficient Synthesis of Glucovanillin and Elucidation of Its Molecular Mechanisms in Ameliorating T2DM via Core Target Modulation and α-Glucosidase Inhibition.Molecules (Basel, Switzerland) · 2026Article
- Open-Source Molecular Docking and AI-Augmented Structure-Based Drug Design: Current Workflows, Challenges, and Opportunities.International journal of molecular sciences · 2026Review
- Repurposing AI for protein interactions and dynamics: opportunities, limitations, and lessons.Frontiers in bioinformatics · 2026Review
- Research progress of artificial intelligence in high-throughput drug screening.Frontiers in pharmacology · 2026Review
- Artificial intelligence-driven drug discovery: a deep learning paradigm shift in pharmaceutical research and development.Frontiers in pharmacology · 2026Review
- Assessing the potential of deep learning for protein-ligand docking.Nature machine intelligence · 2026Article
- Decoding the limits of deep learning in molecular docking for drug discovery.Chemical science · 2025Article
- Beyond rigid docking: deep learning approaches for fully flexible protein-ligand interactions.Briefings in bioinformatics · 2025Review
- Article
- Forging the iron-net: Towards a quantitative understanding of microbial communities via siderophore-mediated interactions.Quantitative biology (Beijing, China) · 2025Article
- Spectra-descriptor-based machine learning for predicting protein-ligand interactions.Chemical science · 2025Article
- MolEM: a unified generative framework for molecular graphs and sequential orders.Briefings in bioinformatics · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Molecular docking, a key technique in structure-based drug design, plays pivotal roles in protein-ligand interaction modeling, hit identification and optimization, in which accurate prediction of protein-ligand binding mode is essential. Conventional docking approaches perform well in redocking tasks with known protein binding pocket conformation in the complex state. However, in real-world docking scenario without knowing the protein binding conformation for a new ligand, accurately modeling the binding complex structure remains challenging as flexible docking is computationally expensive and inaccurate. Typical deep learning-based docking methods do not explicitly consider protein side chain conformations and fail to ensure the physical plausibility and detailed atomic interactions. In this study, we present DiffBindFR, a full-atom diffusion-based flexible docking model that operates over the product space of ligand overall movements and flexibility and pocket side chain torsion changes. We show that DiffBindFR has higher accuracy in producing native-like binding structures with physically plausible and detailed interactions than available docking methods. Furthermore, in the Apo and AlphaFold2 modeled structures, DiffBindFR demonstrates superior advantages in accurate ligand binding pose and protein binding conformation prediction, making it suitable for Apo and AlphaFold2 structure-based drug design. DiffBindFR provides a powerful flexible docking tool for modeling accurate protein-ligand binding structures.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.