ArticleMachine learning2018
Meta-QSAR: a large-scale application of meta-learning to drug design and discovery.
Article in Machine learning, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Compressing Chemistry Reveals Functional Groups.Journal of chemical information and modeling · 2026Article
- Molecular embedding-based algorithm selection in protein-ligand docking.Journal of cheminformatics · 2026Article
- Small data, big challenges: Machine- and deep-learning strategies for data-limited drug discovery.Advanced drug delivery reviews · 2026Review
- Ensemble techniques for predictive modeling of leishmanial activity via molecular fingerprints.BMC medical informatics and decision making · 2025Article
- Machine learning models to identify lead compound and substitution optimization to have derived energetics and conformational stability through docking and MD simulations for sphingosine kinase 1.Molecular diversity · 2025Article
- Digital Alchemy: The Rise of Machine and Deep Learning in Small-Molecule Drug Discovery.International journal of molecular sciences · 2025Review
- OpenML: Insights from 10 years and more than a thousand papers.Patterns (New York, N.Y.) · 2025Article
- Improved Machine Learning Predictions of EC50s Using Uncertainty Estimation from Dose-Response Data.Journal of chemical information and modeling · 2025Article
- Robustifying Likelihoods by Optimistically Re-weighting Data.Journal of the American Statistical Association · 2025Article
- Evaluation of Machine Learning Based QSAR Models for the Classification of Lung Surfactant Inhibitors.Environment & health (Washington, D.C.) · 2024Article
- Cheminformatics and artificial intelligence for accelerating agrochemical discovery.Frontiers in chemistry · 2023Review
- Batched Bayesian Optimization for Drug Design in Noisy Environments.Journal of chemical information and modeling · 2022Article
- Deep generative models for peptide design.Digital discovery · 2022Review
- Limits of Prediction for Machine Learning in Drug Discovery.Frontiers in pharmacology · 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
- Artificial intelligence to deep learning: machine intelligence approach for drug discovery.Molecular diversity · 2021Review
- Virtual screening of anti-HIV1 compounds against SARS-CoV-2: machine learning modeling, chemoinformatics and molecular dynamics simulation based analysis.Scientific reports · 2020Article
- DeepGraphMolGen, a multi-objective, computational strategy for generating molecules with desirable properties: a graph convolution and reinforcement learning approach.Journal of cheminformatics · 2020Article
- An evaluation of machine-learning for predicting phenotype: studies in yeast, rice, and wheat.Machine learning · 2020Article
- Multi-task learning with a natural metric for quantitative structure activity relationship learning.Journal of cheminformatics · 2019Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We investigate the learning of quantitative structure activity relationships (QSARs) as a case-study of meta-learning. This application area is of the highest societal importance, as it is a key step in the development of new medicines. The standard QSAR learning problem is: given a target (usually a protein) and a set of chemical compounds (small molecules) with associated bioactivities (e.g. inhibition of the target), learn a predictive mapping from molecular representation to activity. Although almost every type of machine learning method has been applied to QSAR learning there is no agreed single best way of learning QSARs, and therefore the problem area is well-suited to meta-learning. We first carried out the most comprehensive ever comparison of machine learning methods for QSAR learning: 18 regression methods, 3 molecular representations, applied to more than 2700 QSAR problems. (These results have been made publicly available on OpenML and represent a valuable resource for testing novel meta-learning methods.) We then investigated the utility of algorithm selection for QSAR problems. We found that this meta-learning approach outperformed the best individual QSAR learning method (random forests using a molecular fingerprint representation) by up to 13%, on average. We conclude that meta-learning outperforms base-learning methods for QSAR learning, and as this investigation is one of the most extensive ever comparisons of base and meta-learning methods ever made, it provides evidence for the general effectiveness of meta-learning over base-learning.
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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.