Machine Learning, Molecular Docking, and Dynamics-Based Computational Identification of Potential Inhibitors against Lung Cancer

Abstract

Lung cancer is the most prevalent cause of cancer deaths worldwide. However, its treatment faces a significant hurdle due to the development of resistance. Phytomolecules are an important source of new chemical entities due to their rich chemical diversity. Therefore, a machine learning (ML) model was developed to computationally identify potential inhibitors using a curated data set of 649 phytomolecules with inhibitory activity against lung cancer cell lines. Four distinct ML approaches, including k-nearest neighbor, random forest, support vector machine, and extreme gradient boosting, were used in conjugation with MACCS and Morgan2 fingerprints to generate the models. It was observed that the random forest model developed by using the MACCS fingerprint shows the best performance. To further explore the chemical space and feature importance, k-means clustering, t-SNE analysis, and mean decrease in impurity had been calculated. Simultaneously, ∼400 000 natural products (NPs) retrieved from the COCONUT database were filtered for pharmacokinetic properties and taken for a multistep screening using docking against epidermal growth factor receptor (EGFR) mutant, a therapeutic drug target of lung cancer. Thereafter, the best-performing random forest model was used to predict the antilung cancer potential of the NPs having binding affinity better than the cocrystal ligand. This allowed the identification of 205 potential inhibitors, wherein the molecules with an indolocarbazole scaffold were enriched in top-scoring molecules. The top three indolocarbazole molecules with the lowest binding energy were further evaluated through 100 ns molecular dynamics (MD) simulations, which suggested that these molecules are strong binders. Also, structural similarity analysis against known drugs revealed that these NPs are similar to staurosporine, which demonstrates potent and selective activity against EGFR mutants. Thereby, the consensus analysis employing ML, molecular docking, and dynamics revealed that the molecules having an indolocarbazole scaffold are the most promising NPs that can act as potential inhibitors against lung cancer.