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A Novel Method for Inference of Chemical Compounds of Cycle Index Two with Desired Properties Based on Artificial Neural Networks and Integer Programming

1
Department of Applied Mathematics and Physics, Kyoto University, Kyoto 606-8501, Japan
2
Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji 611-0011, Japan
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Algorithms 2020, 13(5), 124; https://doi.org/10.3390/a13050124
Received: 22 April 2020 / Revised: 13 May 2020 / Accepted: 13 May 2020 / Published: 18 May 2020
(This article belongs to the Special Issue 2020 Selected Papers from Algorithms Editorial Board Members)
Inference of chemical compounds with desired properties is important for drug design, chemo-informatics, and bioinformatics, to which various algorithmic and machine learning techniques have been applied. Recently, a novel method has been proposed for this inference problem using both artificial neural networks (ANN) and mixed integer linear programming (MILP). This method consists of the training phase and the inverse prediction phase. In the training phase, an ANN is trained so that the output of the ANN takes a value nearly equal to a given chemical property for each sample. In the inverse prediction phase, a chemical structure is inferred using MILP and enumeration so that the structure can have a desired output value for the trained ANN. However, the framework has been applied only to the case of acyclic and monocyclic chemical compounds so far. In this paper, we significantly extend the framework and present a new method for the inference problem for rank-2 chemical compounds (chemical graphs with cycle index 2). The results of computational experiments using such chemical properties as octanol/water partition coefficient, melting point, and boiling point suggest that the proposed method is much more useful than the previous method. View Full-Text
Keywords: mixed integer linear programming; QSAR/QSPR; molecular design mixed integer linear programming; QSAR/QSPR; molecular design
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MDPI and ACS Style

Zhu, J.; Wang, C.; Shurbevski, A.; Nagamochi, H.; Akutsu, T. A Novel Method for Inference of Chemical Compounds of Cycle Index Two with Desired Properties Based on Artificial Neural Networks and Integer Programming. Algorithms 2020, 13, 124.

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