ArticleChemical science2025
DiffBP: generative diffusion of 3D molecules for target protein binding.
Article in Chemical science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- Modular Framework for 3D Molecular Generation in Computational Chemistry Applications.Journal of the American Chemical Society · 2026Article
- Targeting the Undruggable: Deep Learning-Driven Design of Peptide Therapeutics in Cancer.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Disentangled diffusion model for 3D molecular generation with protein-ligand interaction priors.Bioinformatics (Oxford, England) · 2026Article
- A Transformer for Reaction-Aware Compound Explorations with GFlowNet in QSAR-Guided Molecular Design.Journal of chemical information and modeling · 2026Article
- A diffusion-based framework for designing molecules in flexible protein pockets.Science advances · 2026Article
- Diffusion Models in Oncology: a Review of Current Applications.Current oncology reports · 2026Review
- From Prompt to Drug: Toward Pharmaceutical Superintelligence.ACS central science · 2026Review
- Generative AI in structure-based drug discovery.BMC biology · 2026Review
- Molecular Design with Artificial Intelligence: Progress and Perspectives for Small Molecules.Chemical reviews · 2026Review
- From machine learning to multimodal models: The AI revolution in enzyme engineering.Biodesign research · 2026Review
- Multimodal Bond Reconstruction toward Generative Molecular Design.Journal of chemical information and modeling · 2026Article
- Structure-informed machine learning for drug discovery: a task-centric perspective.Briefings in bioinformatics · 2026Review
- Apo2Mol: 3D Molecule Generation via Dynamic Pocket-Aware Diffusion Models.Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence · 2026Article
- Architectural good practices for reproducible benchmarking in protein machine learning.Frontiers in bioinformatics · 2026Article
- Artificial intelligence-driven drug discovery: a deep learning paradigm shift in pharmaceutical research and development.Frontiers in pharmacology · 2026Review
- Application and Prospects of Large Language Models in Small-Molecule Drug Discovery.Analytical chemistry · 2025Review
- How evaluation choices distort the outcome of generative drug discovery.Journal of cheminformatics · 2025Article
- Incorporating targeted protein structure in deep learning methods for molecule generation in computational drug design.Chemical science · 2025Review
- SHARP: Generating Synthesizable Molecules via Fragment-Based Hierarchical Action-Space Reinforcement Learning for Pareto Optimization.Journal of chemical information and modeling · 2025Article
- Molecule generation for target protein binding with hierarchical consistency diffusion model.Chemical science · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Generating molecules that bind to specific proteins is an important but challenging task in drug discovery. Most previous works typically generate atoms autoregressively, with element types and 3D coordinates of atoms generated one by one. However, in real-world molecular systems, interactions among atoms are global, spanning the entire molecule, leading to pair-coupled energy function among atoms. With such energy-based consideration, modeling probability should rely on joint distributions rather than sequential conditional ones. Thus, the unnatural sequential auto-regressive approach to molecule generation is prone to violating physical rules, yielding molecules with unfavorable properties. In this study, we propose DiffBP, a generative diffusion model that generates molecular 3D structures, leveraging target proteins as contextual constraints at the full-atom level in a non-autoregressive way. Given a designated 3D protein binding site, our model learns to denoise both element types and 3D coordinates of an entire molecule using an equivariant network. In experimental evaluations, DiffBP demonstrates competitive performance against existing methods, generating molecules with high protein affinity, appropriate molecule sizes, and favorable drug-like profiles. Additionally, we developed a website server for medicinal chemists interested in exploring the art of molecular generation, which is accessible at https://www.manimer.com/moleculeformation/index.
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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.