ArticleJournal of chemical information and modeling2025
Novel GPU Engines for Virtual Screening of Giga-Sized Libraries Identify Inhibitors of Challenging Targets.
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 4 papers.
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Who cites it
4 citing papers in PubMed.
- Elevated conformational dynamics makes ACKR3 activation-prone and G protein-incompetent.bioRxiv : the preprint server for biology · 2026Article
- Pharmacological proximities in the GPCR family discovered using contact-informed amino-acid and binding pocket similarities.bioRxiv : the preprint server for biology · 2026Article
- SLICE (SMARTS and Logic In ChEmistry): fast generation of molecules using advanced chemical synthesis logic and modern coding style.Journal of cheminformatics · 2025Article
- Rules of engagement: Determinants of chemokine receptor activation and selectivity by CCL27 and CCL28.The Journal of biological chemistry · 2025Article
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Authors and funding
6 authors.
Funding
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Abstract
To accelerate virtual ligand screening (VLS) and identify potent drug leads from massive chemical libraries, we developed two GPU-accelerated methods: Rapid Docking GPU Engine (RIDGE) for receptor-based screening and Rapid Isostere Discovery Engine (RIDE) for ligand-based 3D similarity screening. RIDGE performance surpassed or was as good as previously described methods when tested on 102 proteins from the Directory of Useful Decoys, Enhanced (DUD-E). We used RIDGE and RIDE to screen ultralarge virtual libraries against challenging cancer targets, PD-L1 and K-Ras G12D. This led to the discovery of novel inhibitors with single-digit to submicromolar affinities (five for PD-L1, three for K-Ras G12D). Docking scores from our methods were better predictors of binding than conventional VLS. These novel GPU-accelerated methods expand screenable chemical space and successfully identify active hits, even for challenging targets. Further optimization and libraries with higher-molecular-weight cutoffs could further improve targeting of nondruggable proteins.
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Registered trials
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