ReviewMolecules (Basel, Switzerland)2020
Machine Learning Methods in Drug Discovery.
Review in Molecules (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 147 papers, 2 of them syntheses 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
147 citing papers in PubMed, 2 syntheses or guidelines pooled it, 428 citations in OpenAlex.
- Assessing the precision of machine learning for diagnosing pulmonary arterial hypertension: a systematic review and meta-analysis of diagnostic accuracy studies.Frontiers in cardiovascular medicine · 2024Pooled it
- A Systematic Review of Deep Learning Methodologies Used in the Drug Discovery Process with Emphasis on In Vivo Validation.International journal of molecular sciences · 2023Pooled it
- First-in-human, double-blind, randomized phase 1b study of peptide immunotherapy IMCY-0098 in new-onset type 1 diabetes: an exploratory analysis of immune biomarkers.BMC medicine · 2024Trial
- Deep Learning-Based Multi-Cancer Analysis for Predicting Disease-Free Survival Across Multiple Cancer Types.Cancers · 2026Article
- Machine learning-based quantitative structure-activity relationship model for antibiotic prediction and discovery.PLOS digital health · 2026Article
- Machine Learning and Virtual Screening Methods to Discover Potential Cyclin-Dependent Kinase 2 (CDK2) Inhibitors.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Toward an AI Era: Application of Artificial Intelligence in Inclusion Complex Screening.Pharmaceutics · 2026Review
- Harnessing Machine Learning for Accelerated Drug Discovery: Opportunities and Unmet Challenges.Pharmaceuticals (Basel, Switzerland) · 2026Review
- MRI radiomics-based approach to predict pituitary neuroendocrine tumor invasiveness.European radiology experimental · 2026Article
- Navigating the Solubility Landscape of APIs in Deep Eutectic Solvents: A Data-Driven Thermodynamic Taxonomy of Solvation Regimes and Mechanisms.Molecules (Basel, Switzerland) · 2026Article
- AHR/NRF2 Dual Agonist Prediction and Natural Compound Screening Based on Machine Learning: A New Strategy for the Treatment of Atopic Dermatitis.International journal of molecular sciences · 2026Article
- A comprehensive review of intelligent question-answering systems in traditional Chinese medicine based on LLMs.Journal of pharmaceutical analysis · 2026Review
- Identification of alkaline phosphatase as a putative biomarker of anti-NGF treatment-associated arthropathies: Machine learning-assisted analyses of clinical trial data.Osteoarthritis and cartilage open · 2026Article
- Unlocking the potential of computational phenotypic drug discovery: methods, challenges, and future directions.NPJ systems biology and applications · 2026Review
- Big Data for Drug Discovery: Some Historical Landscape, Considerations, and Applications for a Medicinal Chemist.ACS omega · 2026Review
- MolGAN-QRL: a hybrid framework for molecule generation using quantum-enhanced reinforcement learning.Journal of cheminformatics · 2026Article
- Association between the oxidative balance score and all-cause mortality in patients with cardiovascular disease-cancer comorbidity.Journal of health, population, and nutrition · 2026Article
- Integrated dual transcriptome sequencing and experimental validation reveal potential mechanisms of baicalin againstFrontiers in microbiology · 2026Article
- Antimicrobial peptides in the era of resistance: integrating AI-driven design, host-microbe interactions, and nanotechnological delivery.Frontiers in microbiology · 2026Review
- Discovering TEAD4 modulators for hepatocellular carcinoma: a GAN-enabled generative modelling framework.Frontiers in bioinformatics · 2026Article
87 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
5 authors at 3 institutions in 1 country.
Funding
Abstract
The advancements of information technology and related processing techniques have created a fertile base for progress in many scientific fields and industries. In the fields of drug discovery and development, machine learning techniques have been used for the development of novel drug candidates. The methods for designing drug targets and novel drug discovery now routinely combine machine learning and deep learning algorithms to enhance the efficiency, efficacy, and quality of developed outputs. The generation and incorporation of big data, through technologies such as high-throughput screening and high through-put computational analysis of databases used for both lead and target discovery, has increased the reliability of the machine learning and deep learning incorporated techniques. The use of these virtual screening and encompassing online information has also been highlighted in developing lead synthesis pathways. In this review, machine learning and deep learning algorithms utilized in drug discovery and associated techniques will be discussed. The applications that produce promising results and methods will be reviewed.
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.