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

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

#### 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 Î¨ with an ANN so that Î¨( 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) Î¨(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.

Download#### 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 - Volume 3: BIOINFORMATICS,* ISBN 978-989-758-398-8, pages 101-108. DOI: 10.5220/0008876801010108

#### in Bibtex Style

@conference{bioinformatics20,

author={Naveed 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 - 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 - 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