ArticleJournal of advanced research2025
Inactive-enriched machine-learning models exploiting patent data improve structure-based virtual screening for PDL1 dimerizers.
Article in Journal of advanced research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- TRPM8 virtual screening: the essential role of target-specific, inactive-enriched machine-learning scoring functions.Digital discovery · 2026Article
- HERGAI: an artificial intelligence tool for structure-based prediction of hERG inhibitors.Journal of cheminformatics · 2025Article
- CAML: Commutative Algebra Machine Learning─A Case Study on Protein-Ligand Binding Affinity Prediction.Journal of chemical information and modeling · 2025Article
- Cross-disciplinary perspectives on the potential for artificial intelligence across chemistry.Chemical Society reviews · 2025Review
- Rag2Mol: structure-based drug design based on retrieval augmented generation.Briefings in bioinformatics · 2025Article
- SPLIF-Enhanced Attention-Driven 3D CNNs for Precise and Reliable Protein-Ligand Interaction Modeling for METTL3.ACS omega · 2025Article
- InertDB as a generative AI-expanded resource of biologically inactive small molecules from PubChem.Journal of cheminformatics · 2025Article
- Multiscale topology-enabled structure-to-sequence transformer for protein-ligand interaction predictions.Nature machine intelligence · 2024Article
- Comprehensive machine learning boosts structure-based virtual screening for PARP1 inhibitors.Journal of cheminformatics · 2024Article
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5 authors.
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Abstract
introductionSmall-molecule Programmable Cell Death Protein 1/Programmable Death-Ligand 1 (PD1/PDL1) inhibition via PDL1 dimerization has the potential to lead to inexpensive drugs with better cancer patient outcomes and milder side effects. However, this therapeutic approach has proven challenging, with only one PDL1 dimerizer reaching early clinical trials so far. There is hence a need for fast and accurate methods to develop alternative PDL1 dimerizers.
objectivesWe aim to show that structure-based virtual screening (SBVS) based on PDL1-specific machine-learning (ML) scoring functions (SFs) is a powerful drug design tool for detecting PD1/PDL1 inhibitors via PDL1 dimerization.
methodsBy incorporating the latest MLSF advances, we generated and evaluated PDL1-specific MLSFs (classifiers and inactive-enriched regressors) on two demanding test sets.
results60 PDL1-specific MLSFs (30 classifiers and 30 regressors) were generated. Our large-scale analysis provides highly predictive PDL1-specific MLSFs that benefitted from training with large volumes of docked inactives and enabling inactive-enriched regression.
conclusionPDL1-specific MLSFs strongly outperformed generic SFs of various types on this target and are released here without restrictions.
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