Exploring differential evolution for inverse QSAR analysis

Inverse quantitative structure-activity relationship (QSAR) modeling encompasses the generation of compound structures from values of descriptors corresponding to high activity predicted with a given QSAR model. Structure generation proceeds from descriptor coordinates optimized for activity prediction. Herein, we concentrate on the first phase of the inverse QSAR process and introduce a new methodology for coordinate optimization, termed differential evolution (DE), that originated from computer science and engineering. Using simulation and compound activity data, we demonstrate that DE in combination with support vector regression (SVR) yields effective and robust predictions of optimized coordinates satisfying model constraints and requirements. For different compound activity classes, optimized coordinates are obtained that exclusively map to regions of high activity in feature space, represent novel positions for structure generation, and are chemically meaningful. This article is included in the Chemical Information gateway. Science Jürgen Bajorath ( ) Corresponding author: [email protected] : Data Curation, Formal Analysis, Investigation, Methodology, Writing – Review & Editing; : Conceptualization, Author roles: Miyao T Funatsu K Supervision, Writing – Review & Editing; : Conceptualization, Formal Analysis, Supervision, Writing – Original Draft Preparation Bajorath J Competing interests: No competing interests were disclosed. Miyao T, Funatsu K and Bajorath J. How to cite this article: Exploring differential evolution for inverse QSAR analysis [version 1; 2017, (Chem Inf Sci):1285 (doi: ) referees: 3 approved] F1000Research 6 10.12688/f1000research.12228.1 © 2017 Miyao T . This is an open access article distributed under the terms of the , which Copyright: et al Creative Commons Attribution Licence permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The project leading to this report has received funding (for TM) from the Japan Society for the Promotion of Science (JSPS) Grant information: under the JSPS KAKENHI Grant Number 16J05325. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. 31 Jul 2017, (Chem Inf Sci):1285 (doi: ) First published: 6 10.12688/f1000research.12228.1 1,2 1