ReviewNature chemical biology2024
Machine learning in preclinical drug discovery.
Review in Nature chemical biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 85 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
85 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective.Briefings in bioinformatics · 2025Pooled it
- Artificial intelligence-assisted lead optimization in drug discovery: bridging computational advances and translational challenges.Medicinal chemistry research : an international journal for rapid communications on design and mechanisms of action of biologically active agents · 2026Review
- TPPS4 inhibits PEDV by stabilizing viral RNA G-quadruplex and promoting ER stress: a transfer-learning-driven discovery.Journal of virology · 2026Article
- Generation of antifungals to combat drug resistance using language models and diffusion models.Journal of advanced research · 2026Article
- How Advanced Artificial Intelligence Technologies Shape Drug-Drug and Drug-Target Interaction Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Domain-generalized representation learning for cross-chemical-family toxicity prediction.Scientific reports · 2026Article
- Genome assemblies and annotations are not static and need support for tracking their evolution.Briefings in bioinformatics · 2026Review
- Revolutionizing drug discovery from natural products: The roles of artificial intelligence and multi-omics in accelerating innovation.Acta pharmaceutica Sinica. B · 2026Article
- A Reproducible Hierarchical Virtual Screening Framework Integrating Scaffold-Aware Machine Learning, Ensemble Docking, and Molecular Dynamics: Application to IDO1.Journal of chemical information and modeling · 2026Article
- Predicting and explaining poor prognosis in diabetic kidney disease using SHAP-based interpretable machine learning.iScience · 2026Article
- Discovery of TYR inhibitors from de novo molecular generation to dual-track lead optimization: "Competition" between AI and chemists.Science advances · 2026Article
- Benchmarking molecular representations and machine learning algorithms for asymmetric catalysis: a palladium-catalysed decarboxylative asymmetric allylic alkylation case study.Journal of cheminformatics · 2026Article
- Human organoids: Fit for drug discovery?Stem cell reports · 2026Review
- Multi-omics and artificial intelligence for precision drug discovery and potential clinical applications.Signal transduction and targeted therapy · 2026Review
- Applications of transcranial focused ultrasound for primary brain tumors.Neuro-oncology advances · 2026Review
- Machine learning based approaches for structure activity relationship analysis of heparanase inhibitors.Scientific reports · 2026Article
- Machine learning-driven discovery of therapeutic nucleoside hydrogels for periodontitis.International journal of oral science · 2026Article
- Enabling Synthetically Feasible Molecular Editing in Drug Discovery via Reaction-Regulated Graph-Based Genetic Algorithms.JACS Au · 2026Article
- Fine-Tuning a Transformer Model for METTL3 Lead Optimization.ACS bio & med chem Au · 2026Article
- Integrated computational screening of FDA-approved anticancer drugs as novel HPV-16 E6 inhibitors in cervical cancer.Scientific reports · 2026Article
25 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug-discovery and drug-development endeavors are laborious, costly and time consuming. These programs can take upward of 12 years and cost US $2.5 billion, with a failure rate of more than 90%. Machine learning (ML) presents an opportunity to improve the drug-discovery process. Indeed, with the growing abundance of public and private large-scale biological and chemical datasets, ML techniques are becoming well positioned as useful tools that can augment the traditional drug-development process. In this Perspective, we discuss the integration of algorithmic methods throughout the preclinical phases of drug discovery. Specifically, we highlight an array of ML-based efforts, across diverse disease areas, to accelerate initial hit discovery, mechanism-of-action (MOA) elucidation and chemical property optimization. With advances in the application of ML across diverse therapeutic areas, we posit that fully ML-integrated drug-discovery pipelines will define the future of drug-development programs.
Indexed as
Identifiers
39030362What OpenQuestion holds
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.