ArticleJournal of chemical information and modeling2025
MolSnapper: Conditioning Diffusion for Structure-Based Drug Design.
Article in Journal of chemical information and modeling, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Teaching diffusion models physics: reinforcement learning for physically valid diffusion-based docking.Chemical science · 2026Article
- FLOWR: flow matching for structure-aware de novo, interaction- and fragment-based ligand generation.Nature computational science · 2026Article
- ANARCII enables alignment-free antigen receptor numbering using a generalised language model.Communications biology · 2026Article
- An effective fragment-based dual conditional diffusion framework for molecular generation.Briefings in bioinformatics · 2026Article
- Structure-Based Generation of 3D Small-Molecule Drugs: Are We There Yet?Journal of medicinal chemistry · 2025Article
- Incorporating targeted protein structure in deep learning methods for molecule generation in computational drug design.Chemical science · 2025Review
- Benchmarking 3D Structure-Based Molecule Generators.Journal of chemical information and modeling · 2025Article
- Guided multi-objective generative AI to enhance structure-based drug design.Chemical science · 2025Article
- PharmacoForge: pharmacophore generation with diffusion models.Frontiers in bioinformatics · 2025Article
- Article
- Diffusion Models in De Novo Drug Design.Journal of chemical information and modeling · 2024Review
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Authors and funding
4 authors.
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Abstract
Generative models have emerged as potentially powerful methods for molecular design, yet challenges persist in generating molecules that effectively bind to the intended target. The ability to control the design process and incorporate prior knowledge would be highly beneficial for better tailoring molecules to fit specific binding sites. In this paper, we introduce MolSnapper, a novel tool that is able to condition diffusion models for structure-based drug design by seamlessly integrating expert knowledge in the form of 3D pharmacophores. We demonstrate through comprehensive testing on both the CrossDocked and Binding MOAD data sets that our method generates molecules better tailored to fit a given binding site, achieving high structural and chemical similarity to the original molecules. Additionally, MolSnapper yields approximately twice as many valid molecules as alternative methods.
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Registered trials
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