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Pruned Machine Learning Models to Predict Aqueous Solubility.

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Bibliographic Details
Title: Pruned Machine Learning Models to Predict Aqueous Solubility.
Authors: Perryman AL; Department of Pharmacology, Physiology, and Neuroscience, Rutgers University-New Jersey Medical School, Newark, New Jersey 07103, United States., Inoyama D; Department of Pharmacology, Physiology, and Neuroscience, Rutgers University-New Jersey Medical School, Newark, New Jersey 07103, United States., Patel JS; Department of Pharmacology, Physiology, and Neuroscience, Rutgers University-New Jersey Medical School, Newark, New Jersey 07103, United States., Ekins S; Collaborations in Chemistry, Inc., 5616 Hilltop Needmore Road, Fuquay-Varina, North Carolina 27526, United States., Freundlich JS; Department of Pharmacology, Physiology, and Neuroscience, Rutgers University-New Jersey Medical School, Newark, New Jersey 07103, United States.; Division of Infectious Disease, Department of Medicine and the Ruy V. Lourenço Center for the Study of Emerging and Re-emerging Pathogens, Rutgers University-New Jersey Medical School, Newark, New Jersey 07103, United States.
Source: ACS omega [ACS Omega] 2020 Jul 01; Vol. 5 (27), pp. 16562-16567. Date of Electronic Publication: 2020 Jul 01 (Print Publication: 2020).
Publication Type: Journal Article
Journal Info: Publisher: American Chemical Society Country of Publication: United States NLM ID: 101691658 Publication Model: eCollection Cited Medium: Internet ISSN: 2470-1343 (Electronic) Linking ISSN: 24701343 NLM ISO Abbreviation: ACS Omega Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2470-1343
DOI:10.1021/acsomega.0c01251