Computational Identification of Small-Molecule Inhibitors Targeting TEAD2 Palmitoylation Pocket in Lung Cancer

Background

Lung carcinoma is one of the most important health problems in the world and there is a need to develop effective and less toxic therapeutic agents. Computational drug discovery is an efficient approach for discovering new small molecule inhibitors that have potential anticancer activity.

Purpose

In this study, an integrated in silico drug discovery approach was used to search for small molecule lead candidates against lung cancer.

Materials and Methods

The hits identified from the molecular virtual screening were subjected to toxicity and toxicity/cytotoxicity filter, PASS activity filter, drug-likeness, pharmacokinetics, adverse effects, and LD₅₀ filter; then, molecular dynamics analyses (RMSD, RMSF, interactions, and radius of gyration) were performed for stability assessment.

Results

The computational workflow identified two top-ranked compounds that had good binding affinity, low toxicity, and showed potential for anticancer activity as well as good pharmacokinetic features with minimal side effects. Molecular dynamics simulations showed that the protein- ligand interactions and the structure were stable over the course of the simulation.

Conclusion

The discovered compounds showed good inhibitory activity against lung carcinoma and could be a potential treatment candidate. There is a need for additional experimental and clinical research to establish their effectiveness and safety.