Artificial Neural Networks & Random Forest Classification of druggable molecules and disease targets via scoring functions (SFs)

Hudson, I, Leemaqz, S and Abell, A 2019, 'Artificial Neural Networks & Random Forest Classification of druggable molecules and disease targets via scoring functions (SFs)', in Sondoss Elsawah (ed.) Proceedings of the 23rd International Congress on Modelling and Simulation (MODSIM2019), Canberra, Australia, 1-6 December 2019, pp. 28-34.


Document type: Conference Paper
Collection: Conference Papers

Title Artificial Neural Networks & Random Forest Classification of druggable molecules and disease targets via scoring functions (SFs)
Author(s) Hudson, I
Leemaqz, S
Abell, A
Year 2019
Conference name 23rd International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand
Conference location Canberra, Australia
Conference dates 1-6 December 2019
Proceedings title Proceedings of the 23rd International Congress on Modelling and Simulation (MODSIM2019)
Editor(s) Sondoss Elsawah
Publisher MODSIM
Place of publication Australia
Start page 28
End page 34
Total pages 7
Abstract In recent years, machine learning has played an increasing role to help identify druggable molecules. In particular research has shown that random forests (RFs), recursive partitioning (RP), support vector machines (SVMs) and artificial neural networks (ANNs) have been commonly employed in this arena. Expanding disease modifying targets to pharmacological manipulation is vital to human health. Modelling disease targets allow for prediction and prioritisation based on their molecular characteristics and druggability. The aim of this current paper is 2 fold: (i) to propose a computational method to identify druggable disease targets using combinations molecular parameters (MPs) and (ii) to establish which of ANN or RF procedures and which scoring functions best partition molecular and disease target space. Classifications by Artificial Neural Networks (ANNs) and Random Forest (RF) based on 8 molecular parameters (MPs) were performed to classify disease targets with high or low violator scores (using cutpoints 3, 4 or 5), and the 4 traditional parameters of Lipinskis rule of five (Ro5), plus 4 extra parameters (polar surface area (PSA), number of rotatable bonds and rings, N and O atoms, and a choice between 2 alternatives for lipophilicity, the distribution coefficient (log D) and the partition coefficient (log P) (Hudson et al., (2017), Zafar et al., (2013, 2016)).
Subjects Applied Statistics
Bioinformatics
Biostatistics
Keyword(s) Disease targets
score function druggability rules
machine learning
DOI - identifier 10.36334/modsim.2019.A1.hudson
Copyright notice These proceedings are licensed under the terms of the Creative Commons Attribution 4.0 International CC BY License. ndividual MODSIM papers are copyright of the Authors and Modelling and Simulation Society of Australia and New Zealand Inc. (MSSANZ)
ISBN 9780975840092
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