ReviewComputational and structural biotechnology journal2021
A review on machine learning approaches and trends in drug discovery.
Review in Computational and structural biotechnology journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 161 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
161 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Meta-analysis and review of in silico methods in drug discovery - part 1: technological evolution and trends from big data to chemical space.The pharmacogenomics journal · 2025Pooled it
- Soft Computing Approaches toward Density-Based Analysis of Many-Electron Systems.ACS physical chemistry Au · 2026Review
- Cyclization-Modulated Structure and Oxidative Behavior of Methoxy-Substituted Chalcones: Implications for Diesel-Biodiesel Additive Design.ACS omega · 2026Article
- Leveraging Advanced AI Frameworks for Dual PPAR α/γ Agonist Discovery in Alzheimer's Disease.ACS chemical neuroscience · 2026Review
- AI/ML-based computational models for toxicity prediction.Environmental science and pollution research international · 2026Review
- NMR-AI: An Open Platform for NMR-Enhanced Molecular Representations and Physicochemical Property Prediction.Journal of chemical information and modeling · 2026Article
- Explainable Artificial Intelligence (XAI) and Molecular Modeling Techniques to Discover Putative HER2 Inhibitors.International journal of molecular sciences · 2026Article
- Integrating multi-type features and knowledge graph for graded prediction of drug-induced liver injury in humans.PLoS computational biology · 2026Article
- Machine learning-assisted identification and validation of NRP1 inhibitors through molecular docking and dynamics simulations.Scientific reports · 2026Article
- Democratizing Artificial Intelligence in Toxicology: Real-World Applications and Automated Computational Workflows.Chemical research in toxicology · 2026Review
- Benchmarking molecular representations and machine learning algorithms for asymmetric catalysis: a palladium-catalysed decarboxylative asymmetric allylic alkylation case study.Journal of cheminformatics · 2026Article
- Harnessing machine learning models to repurpose drugs targeting HIV-1 integrase, protease, and reverse transcriptase.Scientific reports · 2026Article
- Retinal Transcriptomic Signatures in Sudden Acquired Retinal Degeneration Syndrome (SARDS) and Cancer-Associated Retinopathy (CAR).Animals : an open access journal from MDPI · 2026Article
- A Comparative Review of Artificial Intelligence Applications in Small Molecule Versus Peptide Drug Discovery.International journal of molecular sciences · 2026Review
- AI-Driven Drug Discovery: Focus on Targets for Solid Tumors.Pharmaceutics · 2026Review
- Artificial intelligence drives the identification and screening of novel antibiotics and antimicrobial peptides.Briefings in bioinformatics · 2026Review
- Review
- TheACS omega · 2026Article
- Data-efficient learning for accurate identification of MAPK1 inhibitors using an active meta-deep learning framework.Journal of cheminformatics · 2026Article
- Self-Driving Development of Perfusion Processes for Monoclonal Antibody Production.Biotechnology and bioengineering · 2026Article
101 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
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug discovery aims at finding new compounds with specific chemical properties for the treatment of diseases. In the last years, the approach used in this search presents an important component in computer science with the skyrocketing of machine learning techniques due to its democratization. With the objectives set by the Precision Medicine initiative and the new challenges generated, it is necessary to establish robust, standard and reproducible computational methodologies to achieve the objectives set. Currently, predictive models based on Machine Learning have gained great importance in the step prior to preclinical studies. This stage manages to drastically reduce costs and research times in the discovery of new drugs. This review article focuses on how these new methodologies are being used in recent years of research. Analyzing the state of the art in this field will give us an idea of where cheminformatics will be developed in the short term, the limitations it presents and the positive results it has achieved. This review will focus mainly on the methods used to model the molecular data, as well as the biological problems addressed and the Machine Learning algorithms used for drug discovery in recent years.
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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.