A novel method for inference of chemical compounds with prescribed topological substructures based on integer programming

Clicks: 84
ID: 281882
2020
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #220 of 803 articles by views in arXiv

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 803 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
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 relationships (inverse QSAR/QSPR) analysis, which is to infer chemical structures from given chemical activities/properties. Recently, a novel framework has been proposed for inverse QSAR/QSPR using both artificial neural networks (ANN) and mixed integer linear programming (MILP). This method consists of a prediction phase and an inverse prediction phase. In the first phase, a feature vector $f(G)$ of a chemical graph $G$ is introduced and a prediction function $\psi_{\mathcal{N}}$ on a chemical property $\pi$ is constructed with an ANN $\mathcal{N}$. In the second phase, given a target value $y^*$ of the chemical property $\pi$, a feature vector $x^*$ is inferred by solving an MILP formulated from the trained ANN $\mathcal{N}$ so that $\psi_{\mathcal{N}}(x^*)$ is equal to $y^*$ and then a set of chemical structures $G^*$ such that $f(G^*)= x^*$ is enumerated by a graph enumeration algorithm. The framework has been applied to chemical compounds with a rather abstract topological structure such as acyclic or monocyclic graphs and graphs with a specified polymer topology with cycle index up to 2. In this paper, we propose a new flexible modeling method to the framework so that we can specify a topological substructure of graphs and a partial assignment of chemical elements and bond-multiplicity to a target graph.
Reference Key
nagamochi2020a Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Tatsuya Akutsu; Hiroshi Nagamochi
Journal arXiv
Year 2020
DOI
DOI not found
URL
http://arxiv.org/abs/2010.09203v4
Keywords
cs.ce math.co

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.