ArticleJournal of cheminformatics2019
Multi-task learning with a natural metric for quantitative structure activity relationship learning.
Article in Journal of cheminformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- ADMET-XSpec: A Tool for Systematic Cross-Species Data Integration in ADMET Prediction.Chemical research in toxicology · 2026Article
- Optimizing Multi-Task Learning with Evolutionary Relatedness Metrics for Enhanced QSAR-Based Natural Product Activity Prediction.ACS omega · 2025Article
- OpenML: Insights from 10 years and more than a thousand papers.Patterns (New York, N.Y.) · 2025Article
- AI approaches for the discovery and validation of drug targets.Cambridge prisms. Precision medicine · 2024Review
- Collectively encoding protein properties enriches protein language models.BMC bioinformatics · 2022Article
- Prediction of Combined Sorbent and Catalyst Materials for SE-SMR, Using QSPR and Multitask Learning.Industrial & engineering chemistry research · 2022Article
- Transformational machine learning: Learning how to learn from many related scientific problems.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Imputation of sensory properties using deep learning.Journal of computer-aided molecular design · 2021Article
- Multitask CapsNet: An Imbalanced Data Deep Learning Method for Predicting Toxicants.ACS omega · 2021Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
The goal of quantitative structure activity relationship (QSAR) learning is to learn a function that, given the structure of a small molecule (a potential drug), outputs the predicted activity of the compound. We employed multi-task learning (MTL) to exploit commonalities in drug targets and assays. We used datasets containing curated records about the activity of specific compounds on drug targets provided by ChEMBL. Totally, 1091 assays have been analysed. As a baseline, a single task learning approach that trains random forest to predict drug activity for each drug target individually was considered. We then carried out feature-based and instance-based MTL to predict drug activities. We introduced a natural metric of evolutionary distance between drug targets as a measure of tasks relatedness. Instance-based MTL significantly outperformed both, feature-based MTL and the base learner, on 741 drug targets out of 1091. Feature-based MTL won on 179 occasions and the base learner performed best on 171 drug targets. We conclude that MTL QSAR is improved by incorporating the evolutionary distance between targets. These results indicate that QSAR learning can be performed effectively, even if little data is available for specific drug targets, by leveraging what is known about similar drug targets.
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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.