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

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 Lipinski’s 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)).