Back to Archived Journals » International Journal of High Throughput Screening » Volume 1

Quantitative analysis of aggregation-solubility relationship by in-silico solubility prediction

Authors Mashimo T, Fukunishi Y, Orita M, Katayama N, Fujita S, Nakamura H

Published 28 June 2010 Volume 2010:1 Pages 99—107


Review by Single anonymous peer review

Peer reviewer comments 2

Tadaaki Mashimo1,2, Yoshifumi Fukunishi3, Masaya Orita2,4, Naoko Katayama2,4, Shigeo Fujita2,5, Haruki Nakamura3,6

1Information and Mathematical Science Laboratory Inc., Tokyo, Japan; 2Japan Biological Informatics Consortium (JBIC), Tokyo, Japan; 3Biomedicinal Information Research Center (BIRC), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan; 4Chemistry Research Labs, Drug Discovery Research, Astellas Pharma Inc., Ibaraki, Japan; 5Astellas Research Technology Inc., Ibaraki, Japan; 6Institute for Protein Research, Osaka University, Osaka, Japan

Abstract: Aggregator (frequent hitter) compounds show non-selective binding activity against any target protein and must be removed from the compound library to reduce false positives in drug screening. A previous study suggested that aggregators show high hydrophobicity. The LogS values of aggregators and non-aggregators were estimated by the artificial neural network (ANN) model, the multi-linear regression (MLR) model, and the partial least squares regression (PLS) models, with the weighted learning (WL) method, and the results showed the same trend. The WL method is weighted on the data of the learning set molecules that are similar to the test molecule and improves the prediction accuracy. Bayesian analysis was applied, revealing a simple relationship between aggregation and solubility. Namely, the molecules with LogS > −5 were non-aggregators. In contrast, most of the molecules with LogS < −5 were aggregators. We also made a simple look-up table of probability of aggregation depending on the molecular weight and the number of hetero-atoms.

Keywords: aggregator, frequent hitter, compound library, solubility prediction, generalized-Born accessible-surface area, GBSA

Creative Commons License © 2010 The Author(s). This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at and incorporate the Creative Commons Attribution - Non Commercial (unported, v3.0) License. By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms.