ArticleMolecular pharmaceutics2020
MEDICASCY: A Machine Learning Approach for Predicting Small-Molecule Drug Side Effects, Indications, Efficacy, and Modes of Action.
Article in Molecular pharmaceutics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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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.
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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.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The promising role of new molecular biomarkers in prostate cancer: from coding and non-coding genes to artificial intelligence approaches.Prostate cancer and prostatic diseases · 2022Pooled it
- A clinical decision support system for predicting drug-related adverse effects in pediatric patients with congenital heart disease: Development and retrospective evaluation.The Journal of international medical research · 2026Article
- PHENOCAUZ: Linking Human Symptoms, Drug Side Effects and Efficacy to Their Molecular Causes Using Mendelian Disease Biology.bioRxiv : the preprint server for biology · 2026Article
- GlueFinder: A Data-Driven Framework for the Rational Discovery of Molecular Glues.Journal of chemical information and modeling · 2026Article
- Curated and Structure-Based Drug-Target Interactions Improve Underprediction of Drug Side Effects in Network Models.Journal of chemical information and modeling · 2026Article
- A mode of action protein based approach that characterizes the relationships among most major diseases.Scientific reports · 2025Article
- Article
- Machine Learning Techniques for Predicting Drug-Related Side Effects: A Scoping Review.Pharmaceuticals (Basel, Switzerland) · 2024Article
- Machine learning-based integration of network features and chemical structure of compounds for SARS-CoV-2 drug effect analysis.CPT: pharmacometrics & systems pharmacology · 2024Article
- TTD: Therapeutic Target Database describing target druggability information.Nucleic acids research · 2024Article
- On the additive artificial intelligence-based discovery of nanoparticle neurodegenerative disease drug delivery systems.Beilstein journal of nanotechnology · 2024Article
- Non-Negative matrix factorization combined with kernel regression for the prediction of adverse drug reaction profiles.Bioinformatics advances · 2024Article
- Machine Learning Methods for Small Data Challenges in Molecular Science.Chemical reviews · 2023Review
- An extensive survey on the use of supervised machine learning techniques in the past two decades for prediction of drug side effects.Artificial intelligence review · 2023Article
- Multimodal representation learning for predicting molecule-disease relations.Bioinformatics (Oxford, England) · 2023Article
- PHEVIR: an artificial intelligence algorithm that predicts the molecular role of pathogens in complex human diseases.Scientific reports · 2022Article
- LeMeDISCO is a computational method for large-scale prediction & molecular interpretation of disease comorbidity.Communications biology · 2022Article
- AlphaFold 2: Why It Works and Its Implications for Understanding the Relationships of Protein Sequence, Structure, and Function.Journal of chemical information and modeling · 2021Article
- Prediction of severe adverse events, modes of action and drug treatments for COVID-19's complications.Scientific reports · 2021Article
- Transcriptional Regulation of Cancer Immune Checkpoints: Emerging Strategies for Immunotherapy.Vaccines · 2020Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
To improve the drug discovery yield, a method which is implemented at the beginning of drug discovery that accurately predicts drug side effects, indications, efficacy, and mode of action based solely on the input of the drug's chemical structure is needed. In contrast, extant predictive methods do not comprehensively address these aspects of drug discovery and rely on features derived from extensive, often unavailable experimental information for novel molecules. To address these issues, we developed MEDICASCY, a multilabel-based boosted random forest machine learning method that only requires the small molecule's chemical structure for the drug side effect, indication, efficacy, and probable mode of action target predictions; however, it has comparable or even significantly better performance than existing approaches requiring far more information. In retrospective benchmarking on high confidence predictions, MEDICASCY shows about 78% precision and recall for predicting at least one severe side effect and 72% precision drug efficacy. Experimental validation of MEDICASCY's efficacy predictions on novel molecules shows close to 80% precision for the inhibition of growth in ovarian, breast, and prostate cancer cell lines. Thus, MEDICASCY should improve the success rate for new drug approval. A web service for academic users is available at http://pwp.gatech.edu/cssb/MEDICASCY.
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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.