2D-QSAR, molecular docking, drug-likeness, and ADMET/pharmacokinetic predictions of some non-small cell lung cancer therapeutic agents.

ABSTRACT:

Objectives

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, with nearly 2 million diagnoses and a 17% 5-year survival rate. The aim of this study was to use computer-aided techniques to identify potential therapeutic agents for NSCLC.

Methods

The two dimensional-quantitative structure-activity relationship (2D-QSAR) modeling was employed on some potential NSCLC therapeutic agents to develop a highly predictive model. Molecular docking-based virtual screening were conducted on the same set of compounds to identify potential hit compounds. The pharmacokinetic features of the best hits were evaluated using SWISSADME and pkCSM online web servers, respectively.

Results

The model generated via 2D-QSAR modeling was highly predictive with R²

SUBMITTER: Ibrahim MT 

PROVIDER: S-EPMC9926115 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature