Abstract
DNA repair mechanism is an important mechanism employed by the cancerous cell to survive the DNA damages induced during uncontrolled proliferation of cell and anti-cancer drug treatments. In this context, the Ubiquitin-Specific Proteases (USP1) in complex with Ubiquitin Associated Factor 1(UAF1) plays a key role in the survival of cancerous cell by DNA repair mechanism. Thus, this put forth USP1/UAF1 complex as a striking anti-cancer target for screening of anticancer molecule. The current research is aimed to improve the classification accuracy of the existing bioactivity predictive chemoinformatics model for screening potential active USP1/UAF1 inhibitors from high-throughput screening data. The current study employed feature selection method to extract key molecular descriptors from the publicly available high-throughput screening dataset of small molecules that were used to screen active USP1/UAF1 complex inhibitors. This study proposes an improved predictive machine learning approach using the feature selection technique and two class Linear Discriminant Technique (LDA) algorithm to accurately predict the active novel USP1/UAF1 inhibitor compounds.