ArticleJournal of chemical information and modeling2026
Exploring Chemoinformatics Aspects of Few-Shot Meta-Learning by Example of an Infinite Dilution Activity Coefficient in Ionic Liquid Prediction.
Article in Journal of chemical information and modeling, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Collection of substantial training sets for structure-property modeling often poses a significant challenge, especially for niche sorption systems. This study provides computational experiments with meta-learning that presents a compelling solution to the data scarcity challenge in molecular property prediction. Our analysis utilized infinite dilution activity coefficients for several ionic liquid-solute systems, with coefficient prediction for systems with a particular solute constituting a task. The systems were modeled predominantly using model-agnostic meta-learning (MAML), supported by a study on Reptile and its modified variants. The obtained results provide promising insights into training MAML models by expanding the adaptation set size. Metrics such as
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