TopP–S: Persistent homology‐based multi‐task deep neural networks for simultaneous predictions of partition coefficient and aqueous solubility

• Published in: Journal of computational chemistry Vol. 39; no. 20; pp. 1444 - 1454
• Main Authors: Wu, Kedi, Zhao, Zhixiong, Wang, Renxiao, Wei, Guo‐Wei
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 30.07.2018
• Subjects: aqueous solubility; Artificial neural networks; Coefficients; Datasets; deep neural networks; Homology; Machine learning; Mathematical analysis; Molecular chains; multitask learning; Neural networks; Organic chemistry; partition coefficient; Partitions; persistent homology; Physical properties; Representations; Solubility; topological learning; Topology; topological learning; partition coefficient; multitask learning; aqueous solubility; deep neural networks; persistent homology

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...