ArticleBriefings in bioinformatics2023
KGDiff: towards explainable target-aware molecule generation with knowledge guidance.
Article in Briefings in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- ActivityDiff: a diffusion model with positive and negative activity guidance for de novo drug design.Bioinformatics (Oxford, England) · 2026Article
- Discovery of a triple-site inhibitor targeting bacterial methionyl-tRNA synthetase through combined drug repurposing screening and generative AI-assisted optimization.Nucleic acids research · 2026Article
- Generative design and validation of therapeutic peptides for glioblastoma based on a potential target ATP5A.Briefings in bioinformatics · 2026Article
- Artificial intelligence-driven drug discovery: a deep learning paradigm shift in pharmaceutical research and development.Frontiers in pharmacology · 2026Review
- SGEDiff: a subgraph-enriched diffusion model for structure-based 3D molecular generation.Journal of cheminformatics · 2025Article
- Unified and explainable molecular representation learning for imperfectly annotated data from the hypergraph view.Nature communications · 2025Article
- Structure-aware diffusion model for molecule generation based on K-Nearest Neighbor and equivariant graph neural network.Future medicinal chemistry · 2025Article
- Designing high-affinity 3D drug molecules via geometric spatial perception diffusion model.Briefings in bioinformatics · 2025Article
- A 3D pocket-aware lead optimization model with knowledge guidance and its application for discovery of new glutaminyl cyclase inhibitors.Briefings in bioinformatics · 2025Article
- A 3D generation framework using diffusion model and reinforcement learning to generate multi-target compounds with desired properties.Journal of cheminformatics · 2025Article
- DiffMC-Gen: A Dual Denoising Diffusion Model for Multi-Conditional Molecular Generation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- TargetSA: adaptive simulated annealing for target-specific drug design.Bioinformatics (Oxford, England) · 2024Article
- TamGen: drug design with target-aware molecule generation through a chemical language model.Nature communications · 2024Article
- Diffusion Models in De Novo Drug Design.Journal of chemical information and modeling · 2024Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Designing 3D molecules with high binding affinity for specific protein targets is crucial in drug design. One challenge is that the atomic interaction between molecules and proteins in 3D space has to be taken into account. However, the existing target-aware methods solely model the joint distribution between the molecules and proteins, disregarding the binding affinities between them, which leads to limited performance. In this paper, we propose an explainable diffusion model to generate molecules that can be bound to a given protein target with high affinity. Our method explicitly incorporates the chemical knowledge of protein-ligand binding affinity into the diffusion model, and uses the knowledge to guide the denoising process towards the direction of high binding affinity. Specifically, an SE(3)-invariant expert network is developed to fit the Vina scoring functions and jointly trained with the denoising network, while the domain knowledge is distilled and conveyed from Vina functions to the expert network. An effective guidance is proposed on both continuous atom coordinates and discrete atom types by taking advantages of the gradient of the expert network. Experiments on the benchmark CrossDocked2020 demonstrate the superiority of our method. Additionally, an atom-level explanation of the generated molecules is provided, and the connections with the domain knowledge are established.
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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.