Computational Innovation in Breast Cancer Therapy: Designing Novel Multitarget Quinoxaline Derivatives

Abstract

Breast cancer remains one of the leading causes of cancer-related mortality among women worldwide, highlighting the urgent need for the development of safer and more effective therapeutic agents. In recent years, multitarget drug design has emerged as a promising strategy to overcome drug resistance and improve treatment efficacy by simultaneously modulating multiple signalling pathways involved in cancer progression. The present in silico study aimed to design and evaluate ten novel 3-methyl-2-oxoquinoxaline derivatives as potential multitarget anti-breast cancer agents. The chemical structures were designed using ACD/Labs ChemSketch, followed by the evaluation of physicochemical properties, drug-likeness, and compliance with Lipinski's Rule of Five. Pharmacokinetic and toxicity profiles (ADMET) were predicted using admetSAR, while PASS Online was employed to estimate the probable biological activities of the designed compounds. Based on these screening results, derivatives with favourable pharmaceutical characteristics were selected for molecular docking studies using AutoDock Vina. The binding interactions were further analysed and visualized using BIOVIA Discovery Studio. Several designed quinoxaline derivatives demonstrated favourable binding affinities and stable interactions with multiple breast cancer-associated therapeutic targets, suggesting their potential as promising lead candidates. The integrated computational approach adopted in this study efficiently identified compounds with desirable drug-like properties and multitarget therapeutic potential prior to experimental validation. These findings provide a strong foundation for the synthesis and subsequent biological evaluation of the proposed quinoxaline derivatives as novel candidates for breast cancer therapy.