ReviewDrug discovery today2018
Machine learning in chemoinformatics and drug discovery.
Review in Drug discovery today, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 281 papers.
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
281 citing papers in PubMed.
- DeepFusion-ACSM: an interpretable hybrid ML-DL ensemble model for anticancer small-molecule activity prediction.Journal of computer-aided molecular design · 2026Article
- Machine Learning-Guided Discovery of Natural BACE1 Inhibitors with Enzymatic and Cellular Validation.ACS omega · 2026Article
- Artificial intelligence in toxicology: current advances, challenges and future directions.Archives of toxicology · 2026Review
- Chemically Interpretable Explanations for Molecular Property Prediction via Fragment-Level Shapley Values.Journal of chemical information and modeling · 2026Article
- Multiscale Explainable Machine Learning Reveals Descriptor-Invariant Molecular Determinants of Small-Molecule PD-1/PD-L1 Inhibition.ACS medicinal chemistry letters · 2026Article
- Big Data and Artificial Intelligence in Cancer Drug Discovery: Promise, Challenges, and Emerging Opportunities.Cancers · 2026Review
- Quantitative Structure-Activity Relationship Approaches for Exploring the Anticancer Potential of Flavonoids.Chemistry & biodiversity · 2026Review
- AI-driven drug design: a comprehensive review.Journal of computer-aided molecular design · 2026Review
- Chemical features consistently associated with kinase inhibitor activity: a cross-target SHAP analysis under scaffold-split evaluation.Molecular diversity · 2026Article
- Construction of a multi-label odor prediction model based on molecular structures and olfactory receptor binding profiles with a novel interpretability framework.Analytical sciences : the international journal of the Japan Society for Analytical Chemistry · 2026Article
- Data-Driven Structure-Property Analysis and Rational-Efficient Retrosynthetic Design of 1,2-Dihydronaphtho[2,1‑ACS omega · 2026Article
- Unlabeled but Not Unseen: Cytotoxicity Classification of Re(I) Tricarbonyl Complexes via K‑Means Clustering.ACS omega · 2026Article
- Overrepresentation Bias Leads to Performance Overestimation in Blood-Brain Barrier Permeability Prediction Models: Characterization and Mitigation.Journal of chemical information and modeling · 2026Article
- Machine Learning-Driven QSAR Modeling of FXIa Inhibitors for Virtual Screening and Rational Drug Design.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Discovery of natural RORγt inhibitor using machine learning, virtual screening, and in vivo validation.Journal of advanced research · 2026Article
- Unveiling the Influence of Polyphenols on Mitogen-activated Protein Kinases in Cancer.Cell biochemistry and biophysics · 2026Review
- Identifying the factors influencing long-term care utilization by older adults in China: machine learning analysis.BMC geriatrics · 2026Article
- HP-MoleQ: An Effective Predictive Model for High-Throughput Screening of Food-Derived Hepatoprotective Compounds.Interdisciplinary sciences, computational life sciences · 2026Article
- An Explainable 2D-QSAR Machine Learning Approach for Predicting COX-2 Inhibitory Activity Using Molecular Fingerprints.Pharmaceuticals (Basel, Switzerland) · 2026Article
- From Prompt to Drug: Toward Pharmaceutical Superintelligence.ACS central science · 2026Review
221 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
Abstract
Chemoinformatics is an established discipline focusing on extracting, processing and extrapolating meaningful data from chemical structures. With the rapid explosion of chemical 'big' data from HTS and combinatorial synthesis, machine learning has become an indispensable tool for drug designers to mine chemical information from large compound databases to design drugs with important biological properties. To process the chemical data, we first reviewed multiple processing layers in the chemoinformatics pipeline followed by the introduction of commonly used machine learning models in drug discovery and QSAR analysis. Here, we present basic principles and recent case studies to demonstrate the utility of machine learning techniques in chemoinformatics analyses; and we discuss limitations and future directions to guide further development in this evolving field.
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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.