論文

査読有り
2021年8月

A novel method for inference of acyclic chemical compounds with bounded branch-height based on artificial neural networks and integer programming

Algorithms for Molecular Biology
  • Naveed Ahmed Azam
  • ,
  • Jianshen Zhu
  • ,
  • Yanming Sun
  • ,
  • Yu Shi
  • ,
  • Aleksandar Shurbevski
  • ,
  • Liang Zhao
  • ,
  • Hiroshi Nagamochi
  • ,
  • Tatsuya Akutsu

16
1
記述言語
掲載種別
研究論文(学術雑誌)
DOI
10.1186/s13015-021-00197-2
出版者・発行元
Springer Science and Business Media LLC

<title>Abstract</title>Analysis of chemical graphs is becoming a major research topic in computational molecular biology due to its potential applications to drug design. One of the major approaches in such a study is inverse quantitative structure activity/property relationship (inverse QSAR/QSPR) analysis, which is to infer chemical structures from given chemical activities/properties. Recently, a novel two-phase framework has been proposed for inverse QSAR/QSPR, where in the first phase an artificial neural network (ANN) is used to construct a prediction function. In the second phase, a mixed integer linear program (MILP) formulated on the trained ANN and a graph search algorithm are used to infer desired chemical structures. The framework has been applied to the case of chemical compounds with cycle index up to 2 so far. The computational results conducted on instances with <italic>n</italic> non-hydrogen atoms show that a feature vector can be inferred by solving an MILP for up to <inline-formula><alternatives><tex-math>$$n=40$$</tex-math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML">
<mml:mrow>
<mml:mi>n</mml:mi>
<mml:mo>=</mml:mo>
<mml:mn>40</mml:mn>
</mml:mrow>
</mml:math></alternatives></inline-formula>, whereas graphs can be enumerated for up to <inline-formula><alternatives><tex-math>$$n=15$$</tex-math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML">
<mml:mrow>
<mml:mi>n</mml:mi>
<mml:mo>=</mml:mo>
<mml:mn>15</mml:mn>
</mml:mrow>
</mml:math></alternatives></inline-formula>. When applied to the case of chemical acyclic graphs, the maximum computable diameter of a chemical structure was up to 8. In this paper, we introduce a new characterization of graph structure, called “branch-height” based on which a new MILP formulation and a new graph search algorithm are designed for chemical acyclic graphs. The results of computational experiments using such chemical properties as octanol/water partition coefficient, boiling point and heat of combustion suggest that the proposed method can infer chemical acyclic graphs with around <inline-formula><alternatives><tex-math>$$n=50$$</tex-math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML">
<mml:mrow>
<mml:mi>n</mml:mi>
<mml:mo>=</mml:mo>
<mml:mn>50</mml:mn>
</mml:mrow>
</mml:math></alternatives></inline-formula> and diameter 30.

リンク情報
DOI
https://doi.org/10.1186/s13015-021-00197-2
URL
https://link.springer.com/content/pdf/10.1186/s13015-021-00197-2.pdf
URL
https://link.springer.com/article/10.1186/s13015-021-00197-2/fulltext.html
ID情報
  • DOI : 10.1186/s13015-021-00197-2
  • eISSN : 1748-7188

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