QSAR-guided discovery of novel KRAS inhibitors for lung cancer therapy.

ABSTRACT:

Introduction

KRAS mutations are key oncogenic drivers in lung cancer, yet effective pharmacological targeting has remained a major challenge due to the protein's elusive and dynamic binding pockets. Computational modeling offers a promising route to identify novel inhibitors with improved potency and selectivity.

Methods

A quantitative structure-activity relationship (QSAR) modeling approach was developed to predict the inhibitory potency (pIC₅₀) of KRAS inhibitors and support de novo drug design. Molecular descriptors for 62 inhibitors retrieved from the ChEMBL database (CHEMBL4354832) were computed using Chemopy. Following descriptor normalization and dimensionality reduction, five machine learning algorithm spartial least squares (PLS), random forest (RF

SUBMITTER: Stephen Adebayo O 

PROVIDER: S-EPMC12665777 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature