ArticleJournal of cheminformatics2024
Comprehensive machine learning boosts structure-based virtual screening for PARP1 inhibitors.
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 12 papers.
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Who cites it
12 citing papers in PubMed.
- Enhancing virtual screening of cystathionine β-synthase inhibitors: benchmarking target-specific machine-learning scoring functions against state-of-the-art AI docking and co-folding approaches.Journal of cheminformatics · 2026Article
- V-SYNTHES2-the next generation tool for structure-based virtual screening of giga-scale chemical spaces.npj drug discovery · 2026Article
- PARP1 inhibitors discovery: innovative screening strategies incorporating machine learning and fragment replacement techniques.Molecular diversity · 2026Article
- BOLD-GPCRs: A Transformer-Powered App for Predicting Ligand Bioactivity and Mutational Effects across Class A GPCRs.Journal of chemical information and modeling · 2026Article
- Recent Advances and Emerging Directions in Machine Learning-Based Breast Cancer Drug Discovery: A Comprehensive Review.Breast cancer (Dove Medical Press) · 2026Review
- Experimental and Mechanistic Validation of PARP1pred for Identifying Potent Leads.Computational and structural biotechnology journal · 2026Article
- Review
- V-synthes2 - the Next Generation Tool for Structure-based Virtual Screening of Giga-scale Chemical Spaces.Research square · 2025Article
- HERGAI: an artificial intelligence tool for structure-based prediction of hERG inhibitors.Journal of cheminformatics · 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
- Predicting Inhibition of CDK2 with SAnDReS: The Application of Machine Learning to Navigate the Scoring Function Space.Current medicinal chemistry · 2025Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Poly ADP-ribose polymerase 1 (PARP1) is an attractive therapeutic target for cancer treatment. Machine-learning scoring functions constitute a promising approach to discovering novel PARP1 inhibitors. Cutting-edge PARP1-specific machine-learning scoring functions were investigated using semi-synthetic training data from docking activity-labelled molecules: known PARP1 inhibitors, hard-to-discriminate decoys property-matched to them with generative graph neural networks and confirmed inactives. We further made test sets harder by including only molecules dissimilar to those in the training set. Comprehensive analysis of these datasets using five supervised learning algorithms, and protein-ligand fingerprints extracted from docking poses and ligand only features revealed one highly predictive scoring function. This is the PARP1-specific support vector machine-based regressor, when employing PLEC fingerprints, which achieved a high Normalized Enrichment Factor at the top 1% on the hardest test set (NEF1% = 0.588, median of 10 repetitions), and was more predictive than any other investigated scoring function, especially the classical scoring function employed as baseline.
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