ArticleJournal of chemical information and modeling2019
Learning Drug Functions from Chemical Structures with Convolutional Neural Networks and Random Forests.
Article in Journal of chemical information and modeling, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
- Gaps in AI-Driven Pharmacokinetic Property Prediction for Early Drug Development: A Scoping Review.Journal of chemical information and modeling · 2026Article
- Structure-activity relationship of pharmacophores and toxicophores: the need for clinical strategy.Daru : journal of Faculty of Pharmacy, Tehran University of Medical Sciences · 2024Review
- Drug Discovery in the Age of Artificial Intelligence: Transformative Target-Based Approaches.International journal of molecular sciences · 2024Review
- Development and Evaluation of Conformal Prediction Methods for Quantitative Structure-Activity Relationship.ACS omega · 2024Article
- Machine learning models to predict ligand binding affinity for the orexin 1 receptor.Artificial intelligence chemistry · 2024Article
- Exploring an accurate machine learning model to quickly estimate stability of diverse energetic materials.iScience · 2024Article
- De Novo Generation and Identification of Novel Compounds with Drug Efficacy Based on Machine Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 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
- Leveraging multi-source data to resolve inconsistency across pharmacogenomic datasets in drug sensitivity prediction.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- ChatGPT Chemistry Assistant for Text Mining and the Prediction of MOF Synthesis.Journal of the American Chemical Society · 2023Article
- Machine Learning Methods for Small Data Challenges in Molecular Science.Chemical reviews · 2023Review
- Transferability Based on Drug Structure Similarity in the Automatic Classification of Noncompliant Drug Use on Social Media: Natural Language Processing Approach.Journal of medical Internet research · 2023Article
- mibPOPdb: An online database for microbial biodegradation of persistent organic pollutants.iMeta · 2022Article
- Structure‒tissue exposure/selectivity relationship (STR) correlates with clinical efficacy/safety.Acta pharmaceutica Sinica. B · 2022Article
- Bioactivity assessment of natural compounds using machine learning models trained on target similarity between drugs.PLoS computational biology · 2022Article
- Predicting molecular initiating events using chemical target annotations and gene expression.BioData mining · 2022Article
- Artificial intelligence for COVID-19: battling the pandemic with computational intelligence.Intelligent medicine · 2022Review
- A Genetic Neural Net Model for the Relationship between Pre-School and Attention in Early Childhood.Computational intelligence and neuroscience · 2022Article
- Fighting COVID-19 with Artificial Intelligence.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Deep Neural Networks for QSAR.Methods in molecular biology (Clifton, N.J.) · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Empirical testing of chemicals for drug efficacy costs many billions of dollars every year. The ability to predict the action of molecules in silico would greatly increase the speed and decrease the cost of prioritizing drug leads. Here, we asked whether drug function, defined as MeSH "therapeutic use" classes, can be predicted from only a chemical structure. We evaluated two chemical-structure-derived drug classification methods, chemical images with convolutional neural networks and molecular fingerprints with random forests, both of which outperformed previous predictions that used drug-induced transcriptomic changes as chemical representations. This suggests that the structure of a chemical contains at least as much information about its therapeutic use as the transcriptional cellular response to that chemical. Furthermore, because training data based on chemical structure is not limited to a small set of molecules for which transcriptomic measurements are available, our strategy can leverage more training data to significantly improve predictive accuracy to 83-88%. Finally, we explore use of these models for prediction of side effects and drug-repurposing opportunities and demonstrate the effectiveness of this modeling strategy for multilabel classification.
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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.