ArticleChemical science2016
Multi-objective active machine learning rapidly improves structure-activity models and reveals new protein-protein interaction inhibitors.
Article in Chemical science, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Active learning in pharmaceutical 3D printing: a multi-dataset comparison.Drug delivery and translational research · 2026Article
- PegaPlus─Interactive Machine Learning by Human Observation for Efficient Clustering and Analysis of Structure-Activity Data.Journal of chemical information and modeling · 2026Article
- Improving Generalizability in Whole-Cell Antibiotic Discovery Through Active Learning.bioRxiv : the preprint server for biology · 2026Article
- Multi-omics and artificial intelligence for precision drug discovery and potential clinical applications.Signal transduction and targeted therapy · 2026Review
- Meta-Learning as a Promising Strategy for Lipid Nanoparticle Optimization and Ionizable Lipid Discovery.Nano letters · 2026Article
- Query Matters: How Selection Strategies Influence Active Learning in Drug Discovery.Journal of chemical information and modeling · 2026Article
- Advancing genetic engineering with active learning: theory, implementations and potential opportunities.Briefings in bioinformatics · 2025Review
- Interface-aware molecular generative framework for protein-protein interaction modulators.Journal of cheminformatics · 2024Article
- Screening oral drugs for their interactions with the intestinal transportome via porcine tissue explants and machine learning.Nature biomedical engineering · 2024Article
- Active Learning Approach for Guiding Site-of-Metabolism Measurement and Annotation.Journal of chemical information and modeling · 2024Article
- Finding the most potent compounds using active learning on molecular pairs.Beilstein journal of organic chemistry · 2024Article
- Artificial intelligence for natural product drug discovery.Nature reviews. Drug discovery · 2023Review
- DeepDelta: predicting ADMET improvements of molecular derivatives with deep learning.Journal of cheminformatics · 2023Article
- Machine Learning Strategies for Reaction Development: Toward the Low-Data Limit.Journal of chemical information and modeling · 2023Review
- Evaluation guidelines for machine learning tools in the chemical sciences.Nature reviews. Chemistry · 2022Review
- Data-driven discovery of cardiolipin-selective small molecules by computational active learning.Chemical science · 2022Article
- Spectroscopy Approaches for Food Safety Applications: Improving Data Efficiency Using Active Learning and Semi-supervised Learning.Frontiers in artificial intelligence · 2022Article
- Identification of novel inhibitors of Keap1/Nrf2 by a promising method combining protein-protein interaction-oriented library and machine learning.Scientific reports · 2021Article
- Artificial intelligence in chemistry and drug design.Journal of computer-aided molecular design · 2020Article
- Rethinking drug design in the artificial intelligence era.Nature reviews. Drug discovery · 2020Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Active machine learning puts artificial intelligence in charge of a sequential, feedback-driven discovery process. We present the application of a multi-objective active learning scheme for identifying small molecules that inhibit the protein-protein interaction between the anti-cancer target CXC chemokine receptor 4 (CXCR4) and its endogenous ligand CXCL-12 (SDF-1). Experimental design by active learning was used to retrieve informative active compounds that continuously improved the adaptive structure-activity model. The balanced character of the compound selection function rapidly delivered new molecular structures with the desired inhibitory activity and at the same time allowed us to focus on informative compounds for model adjustment. The results of our study validate active learning for prospective ligand finding by adaptive, focused screening of large compound repositories and virtual compound libraries.
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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.