A Novel Method for the Inverse QSAR/QSPR based on Artificial Neural Networks and Mixed Integer Linear Programming with Guaranteed Admissibility

Naveed Ahmed Azam, Rachaya Chiewvanichakorn, Fan Zhang, Aleksandar Shurbevski, Hiroshi Nagamochi, Tatsuya Akutsu

2020

Abstract

Inverse QSAR/QSPR is a well-known approach for computer-aided drug design. In this study, we propose a novel method for inverse QSAR/QSPR using artificial neural network (ANNs) and mixed integer linear programming. In this method, we introduce a feature function f that converts each chemical compound G into a vector f (G) of several descriptors of G. Next, given a set of chemical compounds along with their chemical properties, we construct a prediction function Ψ with an ANN so that Ψ( f (G)) takes a value nearly equal to a given chemical property for many chemical compounds G in the set. Then, given a target value y* of the chemical property, we conversely infer a chemical structure G* having the desired property y* in the following way. We formulate the problem of finding a vector x* such that (i) Ψ(x*) = y* and (ii) there exists a chemical compound G* such that f (G*) = x* (if one exists over all vectors x* in (i)) as a mixed integer linear programming problem (MILP). In an existing method for the inverse QSAR/QSPR, the second condition (ii) was not guaranteed. For acyclic chemical compounds and some chemical properties such as heat of formation, boiling point, and retention time, we conducted computational experiments.

Paper Citation

in Harvard Style

Azam N., Chiewvanichakorn R., Zhang F., Shurbevski A., Nagamochi H. and Akutsu T. (2020). A Novel Method for the Inverse QSAR/QSPR based on Artificial Neural Networks and Mixed Integer Linear Programming with Guaranteed Admissibility. In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 3: BIOINFORMATICS; ISBN 978-989-758-398-8, SciTePress, pages 101-108. DOI: 10.5220/0008876801010108

in Bibtex Style

@conference{bioinformatics20,
author={Naveed Ahmed Azam and Rachaya Chiewvanichakorn and Fan Zhang and Aleksandar Shurbevski and Hiroshi Nagamochi and Tatsuya Akutsu},
title={A Novel Method for the Inverse QSAR/QSPR based on Artificial Neural Networks and Mixed Integer Linear Programming with Guaranteed Admissibility},
booktitle={Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 3: BIOINFORMATICS},
year={2020},
pages={101-108},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008876801010108},
isbn={978-989-758-398-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 3: BIOINFORMATICS
TI - A Novel Method for the Inverse QSAR/QSPR based on Artificial Neural Networks and Mixed Integer Linear Programming with Guaranteed Admissibility
SN - 978-989-758-398-8
AU - Azam N.
AU - Chiewvanichakorn R.
AU - Zhang F.
AU - Shurbevski A.
AU - Nagamochi H.
AU - Akutsu T.
PY - 2020
SP - 101
EP - 108
DO - 10.5220/0008876801010108
PB - SciTePress