ArticleNature materials2019
Exploiting machine learning for end-to-end drug discovery and development.
Article in Nature materials, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 198 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
198 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The autocrine loop of ALK receptor and ALKAL2 ligand is an actionable target in consensus molecular subtype 1 colon cancer.Journal of experimental & clinical cancer research : CR · 2022Pooled it
- A Nomogram Based on a Collagen Feature Support Vector Machine for Predicting the Treatment Response to Neoadjuvant Chemoradiotherapy in Rectal Cancer Patients.Annals of surgical oncology · 2021Trial
- 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
- Virtual Screening and Molecular Dynamics Simulations Identifying Natural Product-Derived Cathepsin K Inhibitors as Potential Therapeutics for Osteoporosis.International journal of molecular sciences · 2026Article
- Repurposing Antipsychotic Agents in Oncology: Mechanistic Rationale, Emerging Evidence, and Therapeutic Potential.Cancers · 2026Review
- Is AI Capable of Real-World Drug Discovery?Journal of chemical information and modeling · 2026Article
- Atomistic machine learning with irreducible Cartesian natural tensors.Nature communications · 2026Article
- Decoding the Neuroinflammatory Potential of Non-Nutritive Sweeteners Through An Integrative Computational Approach.Neurotoxicity research · 2026Article
- Artificial Intelligence-Driven Nanomedicine: From Drug Formulation and Nanocarrier Design to Clinical Translation.Pharmaceutics · 2026Review
- Host-Directed Antiviral Strategies Against Influenza Viruses: Host Targets, Multi-Omics Approaches and AI-Assisted Discovery.Veterinary sciences · 2026Review
- Machine Learning-Driven QSAR Modeling of FXIa Inhibitors for Virtual Screening and Rational Drug Design.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Emerging paradigms in nanostructured targeted drug delivery systems.Discover nano · 2026Review
- Unveiling the Influence of Polyphenols on Mitogen-activated Protein Kinases in Cancer.Cell biochemistry and biophysics · 2026Review
- Streptococcus-derived CpsA-CpsC fusion protein induces apoptosis in cervical cancer cells through in silico and in vitro methods.Scientific reports · 2026Article
- Customizing Tactile Sensors via Machine Learning-Driven Inverse Design.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Integrative Peptide Drug Development: Chemical Engineering, AI-Driven Design, and Cell-Penetrating Peptides.Pharmaceutics · 2026Review
- Multiscale-Aware Graph Embedding Approach Uncovers LC-61, a Potent Anti-Journal of chemical information and modeling · 2026Article
- Deep learning-enhanced QSAR modeling for predicting developmental neurotoxicity based on molecular initiating events from adverse outcome pathways.Molecular diversity · 2026Article
- Predicting electronic properties of molecules: a stacking ensemble model for HOMO and LUMO energy estimation.RSC advances · 2026Article
- Impact of drug repurposing between 1985 and 2024 on pharmaceutical innovation.Communications medicine · 2026Article
138 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
8 authors.
Funding
Abstract
A variety of machine learning methods such as naive Bayesian, support vector machines and more recently deep neural networks are demonstrating their utility for drug discovery and development. These leverage the generally bigger datasets created from high-throughput screening data and allow prediction of bioactivities for targets and molecular properties with increased levels of accuracy. We have only just begun to exploit the potential of these techniques but they may already be fundamentally changing the research process for identifying new molecules and/or repurposing old drugs. The integrated application of such machine learning models for end-to-end (E2E) application is broadly relevant and has considerable implications for developing future therapies and their targeting.
Indexed as
Identifiers
What 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.