Literature DB >> 15154768

ESOL: estimating aqueous solubility directly from molecular structure.

John S Delaney1.   

Abstract

This paper describes a simple method for estimating the aqueous solubility (ESOL--Estimated SOLubility) of a compound directly from its structure. The model was derived from a set of 2874 measured solubilities using linear regression against nine molecular properties. The most significant parameter was calculated logP(octanol), followed by molecular weight, proportion of heavy atoms in aromatic systems, and number of rotatable bonds. The model performed consistently well across three validation sets, predicting solubilities within a factor of 5-8 of their measured values, and was competitive with the well-established "General Solubility Equation" for medicinal/agrochemical sized molecules.

Entities:  

Year:  2004        PMID: 15154768     DOI: 10.1021/ci034243x

Source DB:  PubMed          Journal:  J Chem Inf Comput Sci        ISSN: 0095-2338


  84 in total

1.  Substructural fragments: an universal language to encode reactions, molecular and supramolecular structures.

Authors:  A Varnek; D Fourches; F Hoonakker; V P Solov'ev
Journal:  J Comput Aided Mol Des       Date:  2005-11-16       Impact factor: 3.686

2.  Rank order entropy: why one metric is not enough.

Authors:  Margaret R McLellan; M Dominic Ryan; Curt M Breneman
Journal:  J Chem Inf Model       Date:  2011-08-29       Impact factor: 4.956

3.  Machine Learning in a Molecular Modeling Course for Chemistry, Biochemistry, and Biophysics Students.

Authors:  Jacob M Remington; Jonathon B Ferrell; Marlo Zorman; Adam Petrucci; Severin T Schneebeli; Jianing Li
Journal:  Biophysicist (Rockv)       Date:  2020-08-13

4.  Exploratory analysis of kinetic solubility measurements of a small molecule library.

Authors:  Rajarshi Guha; Thomas S Dexheimer; Aimee N Kestranek; Ajit Jadhav; Andrew M Chervenak; Michael G Ford; Anton Simeonov; Gregory P Roth; Craig J Thomas
Journal:  Bioorg Med Chem       Date:  2011-05-13       Impact factor: 3.641

5.  Deep architectures and deep learning in chemoinformatics: the prediction of aqueous solubility for drug-like molecules.

Authors:  Alessandro Lusci; Gianluca Pollastri; Pierre Baldi
Journal:  J Chem Inf Model       Date:  2013-07-02       Impact factor: 4.956

6.  Multi-channel GCN ensembled machine learning model for molecular aqueous solubility prediction on a clean dataset.

Authors:  Chenglong Deng; Li Liang; Guomeng Xing; Yi Hua; Tao Lu; Yanmin Zhang; Yadong Chen; Haichun Liu
Journal:  Mol Divers       Date:  2022-06-23       Impact factor: 2.943

7.  Identification of 2-arylquinazolines with alkyl-polyamine motifs as potent antileishmanial agents: synthesis and biological evaluation studies.

Authors:  Anjila Kumari; Tara Jaiswal; Vinay Kumar; Neha Hura; Gulshan Kumar; Neerupudi Kishore Babu; Ayan Acharya; Pradyot K Roy; Sankar K Guchhait; Sushma Singh
Journal:  RSC Med Chem       Date:  2022-01-06

8.  Identifying selective agonists targeting LXRβ from terpene compounds of alismatis rhizoma.

Authors:  Chuanjiong Lin; Jianzong Li; Chuanfang Wu; Jinku Bao
Journal:  J Mol Model       Date:  2021-02-22       Impact factor: 1.810

9.  Selecting, Acquiring, and Using Small Molecule Libraries for High-Throughput Screening.

Authors:  Sivaraman Dandapani; Gerard Rosse; Noel Southall; Joseph M Salvino; Craig J Thomas
Journal:  Curr Protoc Chem Biol       Date:  2012-09-01

10.  Development of an allosteric inhibitor class blocking RNA elongation by the respiratory syncytial virus polymerase complex.

Authors:  Robert M Cox; Mart Toots; Jeong-Joong Yoon; Julien Sourimant; Barbara Ludeke; Rachel Fearns; Elyse Bourque; Joseph Patti; Edward Lee; John Vernachio; Richard K Plemper
Journal:  J Biol Chem       Date:  2018-09-11       Impact factor: 5.157

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