ArticleJournal of cheminformatics2024
Pocket Crafter: a 3D generative modeling based workflow for the rapid generation of hit molecules in drug discovery.
Article in Journal of cheminformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- A diffusion model conditioned on compound bioactivity profiles for generating high-content images.Scientific reports · 2026Article
- Integrating molecular generation and fingerprints transferring for single-molecule theranostics targeting endoplasmic reticulum stress.Journal of advanced research · 2026Article
- Deep learning reveals endogenous sterols as allosteric modulators of the GPCR-Gα interface.eLife · 2025Article
- Rag2Mol: structure-based drug design based on retrieval augmented generation.Briefings in bioinformatics · 2025Article
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Authors and funding
7 authors.
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Abstract
We present a user-friendly molecular generative pipeline called Pocket Crafter, specifically designed to facilitate hit finding activity in the drug discovery process. This workflow utilized a three-dimensional (3D) generative modeling method Pocket2Mol, for the de novo design of molecules in spatial perspective for the targeted protein structures, followed by filters for chemical-physical properties and drug-likeness, structure-activity relationship analysis, and clustering to generate top virtual hit scaffolds. In our WDR5 case study, we acquired a focused set of 2029 compounds after a targeted searching within Novartis archived library based on the virtual scaffolds. Subsequently, we experimentally profiled these compounds, resulting in a novel chemical scaffold series that demonstrated activity in biochemical and biophysical assays. Pocket Crafter successfully prototyped an effective end-to-end 3D generative chemistry-based workflow for the exploration of new chemical scaffolds, which represents a promising approach in early drug discovery for hit identification.
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