Journal
JOURNAL OF COMPUTATIONAL CHEMISTRY
Volume 39, Issue 20, Pages 1444-1454Publisher
WILEY
DOI: 10.1002/jcc.25213
Keywords
persistent homology; partition coefficient; aqueous solubility; multitask learning; deep neural networks; topological learning
Categories
Funding
- NSF [DMS-1721024, IIS-1302285]
- MSU Center for Mathematical Molecular Biosciences Initiative
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [1302285] Funding Source: National Science Foundation
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Aqueous solubility and partition coefficient are important physical properties of small molecules. Accurate theoretical prediction of aqueous solubility and partition coefficient plays an important role in drug design and discovery. The prediction accuracy depends crucially on molecular descriptors which are typically derived from a theoretical understanding of the chemistry and physics of small molecules. This work introduces an algebraic topology-based method, called element-specific persistent homology (ESPH), as a new representation of small molecules that is entirely different from conventional chemical and/or physical representations. ESPH describes molecular properties in terms of multiscale and multicomponent topological invariants. Such topological representation is systematical, comprehensive, and scalable with respect to molecular size and composition variations. However, it cannot be literally translated into a physical interpretation. Fortunately, it is readily suitable for machine learning methods, rendering topological learning algorithms. Due to the inherent correlation between solubility and partition coefficient, a uniform ESPH representation is developed for both properties, which facilitates multi-task deep neural networks for their simultaneous predictions. This strategy leads to a more accurate prediction of relatively small datasets. A total of six datasets is considered in this work to validate the proposed topological and multitask deep learning approaches. It is demonstrated that the proposed approaches achieve some of the most accurate predictions of aqueous solubility and partition coefficient. Our software is available online at . (c) 2018 Wiley Periodicals, Inc.
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