Search Results for author: Kazuya Haraguchi

Found 5 papers, 2 papers with code

Molecular Design Based on Integer Programming and Splitting Data Sets by Hyperplanes

no code implementations27 Apr 2023 Jianshen Zhu, Naveed Ahmed Azam, Kazuya Haraguchi, Liang Zhao, Hiroshi Nagamochi, Tatsuya Akutsu

A novel framework for designing the molecular structure of chemical compounds with a desired chemical property has recently been proposed.

Molecular Design Based on Integer Programming and Quadratic Descriptors in a Two-layered Model

1 code implementation13 Sep 2022 Jianshen Zhu, Naveed Ahmed Azam, Shengjuan Cao, Ryota Ido, Kazuya Haraguchi, Liang Zhao, Hiroshi Nagamochi, Tatsuya Akutsu

A set of graph theoretical descriptors in the feature function plays a key role to derive a compact formulation of such an MILP.

A Method for Inferring Polymers Based on Linear Regression and Integer Programming

no code implementations24 Aug 2021 Ryota Ido, Shengjuan Cao, Jianshen Zhu, Naveed Ahmed Azam, Kazuya Haraguchi, Liang Zhao, Hiroshi Nagamochi, Tatsuya Akutsu

For this, we introduce a new way of representing a polymer as a form of monomer and define new descriptors that feature the structure of polymers.

regression

Molecular Design Based on Artificial Neural Networks, Integer Programming and Grid Neighbor Search

no code implementations23 Aug 2021 Naveed Ahmed Azam, Jianshen Zhu, Kazuya Haraguchi, Liang Zhao, Hiroshi Nagamochi, Tatsuya Akutsu

In the framework, a chemical graph with a target chemical value is inferred as a feasible solution of a mixed integer linear program that represents a prediction function and other requirements on the structure of graphs.

An Inverse QSAR Method Based on Linear Regression and Integer Programming

1 code implementation6 Jul 2021 Jianshen Zhu, Naveed Ahmed Azam, Kazuya Haraguchi, Liang Zhao, Hiroshi Nagamochi, Tatsuya Akutsu

In the framework, we first define a feature vector $f(C)$ of a chemical graph $C$ and construct an ANN that maps $x=f(C)$ to a predicted value $\eta(x)$ of a chemical property $\pi$ to $C$.

regression

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