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

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.

Integrating Five Feature Types Extracted From Ultrasonograms to Improve the Prediction of Thyroid Papillary Carcinoma

“…The binary prediction model was evaluated for its sensitivity (Sn), specificity (Sp) and accuracy (Acc). The Matthews' correlation coefficient has been widely used to evaluate biomedical prediction models based on protein sequences [23], small molecules [24] and images [25]. It was defined as MCC = (TP×TN-FP×FN)/sqrt((TP + FP) ×(TP + FN) ×(TN + FP) ×(TN + FN)), where sqrt(x) was the squared root of x.…”