ArticlePLoS computational biology2022
A machine learning model trained on a high-throughput antibacterial screen increases the hit rate of drug discovery.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Artificial Intelligence-Guided Phenotypic Drug Repurposing Against Streptococcus pneumoniae.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Machine Learning-Guided Discovery of Bacterial-Selective Membrane-Active Compounds Reveals Mechanistic Bias in Antibiotic Training Datasets.bioRxiv : the preprint server for biology · 2026Article
- Generation of antifungals to combat drug resistance using language models and diffusion models.Journal of advanced research · 2026Article
- Improving Generalizability in Whole-Cell Antibiotic Discovery Through Active Learning.bioRxiv : the preprint server for biology · 2026Article
- Machine Learning-Guided Discovery of Bacterial-Selective Membrane-Active Compounds Reveals Mechanistic Bias in Antibiotic Training Datasets.bioRxiv : the preprint server for biology · 2026Article
- Harnessing artificial intelligence for antimicrobial discovery and optimization.Current opinion in microbiology · 2026Review
- Integrating machine learning and artificial intelligence in the management of Acinetobacter infections: a narrative review.Infection · 2026Review
- Machine learning-based discovery of GW3965 as a therapeutic compound against invasive emm92-type group A Streptococcus.npj antimicrobials and resistance · 2026Article
- Combating Antibacterial Resistance: The Integrative Role of Artificial Intelligence in Bio-Based Product Development.Antibiotics (Basel, Switzerland) · 2026Review
- Reclaiming the Microbial Battlefield: Adjuvant Strategies to Overcome Antibiotic Resistance.Microorganisms · 2026Review
- Machine learning-based discovery of GW3965 as a therapeutic compound against invasive emm92-type group A Streptococcus.Research square · 2026Article
- Artificial Intelligence as a Catalyst for Antimicrobial Discovery: From Predictive Models to De Novo Design.Microorganisms · 2026Review
- Small data, big challenges: Machine- and deep-learning strategies for data-limited drug discovery.Advanced drug delivery reviews · 2026Review
- Vancomycin resistance in gram-positive infections: evolutionary strategies of survival.Archives of microbiology · 2026Review
- Antimicrobial Resistance: The Answers.British journal of biomedical science · 2026Review
- The WHO priority list of antibiotic-resistant bacteria: challenges and opportunities for next-generation antimicrobial development.Frontiers in pharmacology · 2026Review
- A dual functional inhibitory molecule to combat GyrA-ParC and associated mutants in fluoroquinolone-resistantFrontiers in bioinformatics · 2026Article
- Machine learning-based virtual screening and density functional theory characterisation of natural inhibitors targeting mutant PBP2x in Streptococcus pneumoniae.Scientific reports · 2025Article
- Integrating artificial intelligence into small molecule development for precision cancer immunomodulation therapy.npj drug discovery · 2025Review
- Machine learning and cheminformatics-based Identification of lichen-derived compounds targeting mutant PBP4Molecular diversity · 2025Article
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Authors and funding
7 authors.
Funding
Abstract
Screening for novel antibacterial compounds in small molecule libraries has a low success rate. We applied machine learning (ML)-based virtual screening for antibacterial activity and evaluated its predictive power by experimental validation. We first binarized 29,537 compounds according to their growth inhibitory activity (hit rate 0.87%) against the antibiotic-resistant bacterium Burkholderia cenocepacia and described their molecular features with a directed-message passing neural network (D-MPNN). Then, we used the data to train an ML model that achieved a receiver operating characteristic (ROC) score of 0.823 on the test set. Finally, we predicted antibacterial activity in virtual libraries corresponding to 1,614 compounds from the Food and Drug Administration (FDA)-approved list and 224,205 natural products. Hit rates of 26% and 12%, respectively, were obtained when we tested the top-ranked predicted compounds for growth inhibitory activity against B. cenocepacia, which represents at least a 14-fold increase from the previous hit rate. In addition, more than 51% of the predicted antibacterial natural compounds inhibited ESKAPE pathogens showing that predictions expand beyond the organism-specific dataset to a broad range of bacteria. Overall, the developed ML approach can be used for compound prioritization before screening, increasing the typical hit rate of drug discovery.
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