ArticleJournal of cheminformatics2026
Prediction of intrinsic solubility for drug-like organic compounds using automated network optimizer (ANO) for physicochemical feature and hyperparameter optimization.
Article in Journal of cheminformatics, 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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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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6 authors.
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Abstract
Accurate prediction of aqueous solubility remains a critical challenge in the chemical and pharmaceutical industries, significantly influencing drug development and delivery. This study revisits this well-explored area by leveraging the advanced capabilities of modern computational resources. We apply an automated network optimizer model that integrates dual optimization processes for molecular features and hyperparameters, streamlining the traditionally complex hyperparameter search while providing an efficient interpretation of molecular properties. By employing feature optimization techniques, our deep neural network model demonstrates improvements in both the speed and accuracy of molecular property predictions, achieving an average performance of R
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