Developing a predictive QSAR model for FGFR-1 inhibitors: integrating computational and experimental validation.

The traditional drug discovery process is often lengthy, costly, and characterized by a high failure rate. There is a pressing need for innovative strategies to optimize this process and improve the chances of identifying effective therapeutic candidates. This study aims to utilize computational methods to develop a quantitative structure-activity relationship (QSAR) model that predicts the inhibitory activity of compounds against Fibroblast Growth Factor Receptor 1 (FGFR-1), which is associated with various cancers, including lung and breast cancer. The QSAR model was developed using multiple linear regression (MLR) on a dataset of 1779 compounds from the ChEMBL database. The dataset was curated, and molecular descriptors were calculated using Alvadesc software. Feature selection techniques refined the dataset, and the model's predictive capability was validated through 10-fold cross-validation and external validation with a test set. In silico validation was further performed using molecular docking and molecular dynamics simulations. Additionally, in vitro validation was conducted using MTT, wound healing, and clonogenic assays on A549 (lung cancer), MCF-7 (breast cancer), HEK-293 (normal human embryonic kidney), and VERO (normal African green monkey kidney) cell lines. The QSAR model exhibited strong predictive performance with an R