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 possibl

SUBMITTER: Wiercioch M 

PROVIDER: S-EPMC6539940 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature