Exploring the Potential of Spherical Harmonics and PCVM for Compounds Activity Prediction.

Magdalena Wiercioch

doi:10.3390/ijms20092175

Abstract

Biologically active chemical compounds may provide remedies for several diseases. Meanwhile, Machine Learning techniques applied to Drug Discovery, which are cheaper and faster than wet-lab experiments, have the capability to more effectively identify molecules with the expected pharmacological activity. Therefore, it is urgent and essential to develop more representative descriptors and reliable classification methods to accurately predict molecular activity. In this paper, we investigate the potential of a novel representation based on Spherical Harmonics fed into Probabilistic Classification Vector Machines classifier, namely SHPCVM, to compound the activity prediction task. We make use of representation learning to acquire the features which describe the molecules as precise as possible. To verify the performance of SHPCVM ten-fold cross-validation tests are performed on twenty-one G protein-coupled receptors (GPCRs). Experimental outcomes (accuracy of 0.86) assessed by the classification accuracy, precision, recall, Matthews’ Correlation Coefficient and Cohen’s kappa reveal that using our Spherical Harmonics-based representation which is relatively short and Probabilistic Classification Vector Machines can achieve very satisfactory performance results for GPCRs.

Highlights

Rational drug discovery aims at the identification of ligands that act on single or multiple drug targets [1,2,3]
We introduce a novel methodology that involves Probabilistic Classification Vector Machines (PCVM) and Spherical Harmonics-based descriptor which we call SHPCVM
We propose a novel molecular activity prediction method called SHPCVM

Summary

Introduction

Rational drug discovery aims at the identification of ligands that act on single or multiple drug targets [1,2,3]. In order to find the desired candidates, several computational approaches are required which enable to predict drug-like properties. Take for instance virtual screening [4], which has its roots in cheminformatics and performs the rapid in silico assessment of large libraries of chemical structures to identify those most likely to bind to a drug target. One may observe the success and possible new opportunities with regards to ligand-based virtual screening [5]. In this modern era of computational technological advancement, machine learning has been extensively applied to predict the activity of new candidate compounds. Liu et al have constructed ensembles to identify Piwi-Interacting RNAs [15]

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