ArticleACS central science2024
Machine Learning Assisted Hit Prioritization for High Throughput Screening in Drug Discovery.
Article in ACS central science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed.
- Benchmarking molecular representations and machine learning algorithms for asymmetric catalysis: a palladium-catalysed decarboxylative asymmetric allylic alkylation case study.Journal of cheminformatics · 2026Article
- Biophysical Sensing Tools in Drug Discovery: Integrating Kinetics, Thermodynamics, Cellular Target Engagement and Structure.Sensors (Basel, Switzerland) · 2026Review
- Relating Model Performance to Embedding Distributions in Molecular Machine Learning.Journal of chemical information and modeling · 2026Article
- Small data, big challenges: Machine- and deep-learning strategies for data-limited drug discovery.Advanced drug delivery reviews · 2026Review
- Toward the Chemoenzymatic Synthesis of DNA-Encoded Libraries.ACS central science · 2026Review
- Advancing Drug Discovery with AI: Machine and Deep Learning Strategies for Target Identification and Precision Nanomedicine.International journal of nanomedicine · 2026Review
- Deep learning-based discovery of tetrahydrocarbazoles as broad-spectrum antitumor agents and click-activated strategy for targeted cancer therapy.Acta pharmaceutica Sinica. B · 2026Article
- AI-enabled language models (LMs) to large language models (LLMs) and multimodal large language models (MLLMs) in drug discovery and development.Journal of advanced research · 2025Review
- Improving Machine Learning Classification Predictions through SHAP and Features Analysis Interpretation.Journal of chemical information and modeling · 2025Article
- High-throughput screening identifies novel chemical scaffolds targeting Leishmania donovani parasites.World journal of microbiology & biotechnology · 2025Article
- Machine Learning for Toxicity Prediction Using Chemical Structures: Pillars for Success in the Real World.Chemical research in toxicology · 2025Review
- Rethinking Biomedical Titanium Alloy Design: A Review of Challenges from Biological and Manufacturing Perspectives.Advanced healthcare materials · 2025Review
- Machine Learning-Driven Data Valuation for Optimizing High-Throughput Screening Pipelines.Journal of chemical information and modeling · 2024Article
- Towards maximizing biomass and lipid productivity: high-throughput screening assay for prospecting heterotrophic growth for new microalgal isolates.Microbial cell factories · 2024Article
- AI-Powered Mining of Highly Customized and Superior ESIPT-Based Fluorescent Probes.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- DrugGym: A testbed for the economics of autonomous drug discovery.bioRxiv : the preprint server for biology · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Efficient prioritization of bioactive compounds from high throughput screening campaigns is a fundamental challenge for accelerating drug development efforts. In this study, we present the first data-driven approach to simultaneously detect assay interferents and prioritize true bioactive compounds. By analyzing the learning dynamics during training of a gradient boosting model on noisy high throughput screening data using a novel formulation of sample influence, we are able to distinguish between compounds exhibiting the desired biological response and those producing assay artifacts. Therefore, our method enables false positive and true positive detection without relying on prior screens or assay interference mechanisms, making it applicable to any high throughput screening campaign. We demonstrate that our approach consistently excludes assay interferents with different mechanisms and prioritizes biologically relevant compounds more efficiently than all tested baselines, including a retrospective case study simulating its use in a real drug discovery campaign. Finally, our tool is extremely computationally efficient, requiring less than 30 s per assay on low-resource hardware. As such, our findings show that our method is an ideal addition to existing false positive detection tools and can be used to guide further pharmacological optimization after high throughput screening campaigns.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.