Pruned Machine Learning Models to Predict Aqueous Solubility-Reference-Cited by-同舟云学术

Pruned Machine Learning Models to Predict Aqueous Solubility

Published:2020-07-01 Issue:27 Volume:5 Page:16562-16567
ISSN:2470-1343
Container-title:ACS Omega
language:en
Short-container-title:ACS Omega

Author:

Perryman Alexander L.¹,Inoyama Daigo¹,Patel Jimmy S.¹,Ekins Sean²^ORCID,Freundlich Joel S.¹³^ORCID

Affiliation:

1. Department of Pharmacology, Physiology, and Neuroscience, Rutgers University—New Jersey Medical School, Newark, New Jersey 07103, United States

2. Collaborations in Chemistry, Inc., 5616 Hilltop Needmore Road, Fuquay-Varina, North Carolina 27526, United States

3. 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

Funder

National Institute of Allergy and Infectious Diseases

Publisher

American Chemical Society (ACS)

Subject

General Chemical Engineering,General Chemistry

Link

http://pubs.acs.org/doi/pdf/10.1021/acsomega.0c01251

Reference35 articles.

1. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings

2. Optimizing solubility: Kinetic versus thermodynamic solubility temptations and risks

3. Biological assay challenges from compound solubility: strategies for bioassay optimization

4. Prediction of drug solubility from molecular structure using a drug-like training set

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