<abstract xmlns="http://www.w3.org/1999/xhtml"><p>In this paper, we describe how several soft computing tools can be used to assist in high throughput screening of potential drug candidates. Individual small molecules (ligands) are assessed for their potential to bind to specific proteins (receptors). Committees of multilayer networks are used to classify protein-ligand complexes as good binders or bad binders, based on selected chemical descriptors. The novel aspects of this paper include the use of statistical analyses on the weights of single layer networks to select the appropriate descriptors, the use of Monte Carlo cross-validation to provide confidence measures of network performance (and also to identify problems in the data), the addition of new chemical descriptors to improve network accuracy, and the use of Self Organizing Maps to analyze the performance of the trained network and identify anomalies. We demonstrate the procedures on a large practical data set, and use them to discover a promising characteristic of the data. We also perform virtual screenings with the trained networks on a number of benchmark sets and analyze the results.</p></abstract>

In this paper, we describe how several soft computing tools can be used to assist in high throughput screening of potential drug candidates. Individual small molecules (ligands) are assessed for their potential to bind to specific proteins (receptors). Committees of multilayer networks are used to classify protein-ligand complexes as good binders or bad binders, based on selected chemical descriptors. The novel aspects of this paper include the use of statistical analyses on the weights of single layer networks to select the appropriate descriptors, the use of Monte Carlo cross-validation to provide confidence measures of network performance (and also to identify problems in the data), the addition of new chemical descriptors to improve network accuracy, and the use of Self Organizing Maps to analyze the performance of the trained network and identify anomalies. We demonstrate the procedures on a large practical data set, and use them to discover a promising characteristic of the data. We also perform virtual screenings with the trained networks on a number of benchmark sets and analyze the results.

Soft Computing Tools for Virtual Drug Discovery

Martin Hagan School of Electrical and Computer Engineering

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Soft Computing Tools for Virtual Drug Discovery"}

In this paper, we describe how several soft computing tools can be used to assist in high throughput screening of potential drug candidates. Individual small...

Soft Computing Tools for Virtual Drug Discovery

Published Online: Feb 09, 2018

Page range: 173 - 189

Received: Sep 03, 2017

Accepted: Aug 30, 2017

DOI: https://doi.org/10.1515/jaiscr-2018-0012

Keywords
drug discovery, virtual screening, multilayer network, SOM

© 2018 Daniel Hagan et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Soft Computing Tools for Virtual Drug Discovery

Published Online: Feb 09, 2018

Page range: 173 - 189

Received: Sep 03, 2017

Accepted: Aug 30, 2017

DOI: https://doi.org/10.1515/jaiscr-2018-0012

Keywordsdrug discovery, virtual screening, multilayer network, SOM

© 2018 Daniel Hagan et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Keywords
drug discovery, virtual screening, multilayer network, SOM