Sequential virtual screening collaborated with machine-learning strategies for the discovery of precise medicine against non-small cell lung cancer

Abstract

Dysregulation of MAPK pathway receptors are crucial in causing uncontrolled cell proliferation in many cancer types including non-small cell lung cancer. Due to the complications in targeting the upstream components, MEK is an appealing target to diminish this pathway activity. Hence, we have aimed to discover potent MEK inhibitors by integrating virtual screening and machine learning-based strategies. Preliminary screening was conducted on 11,808 compounds using the cavity-based pharmacophore model AADDRRR. Further, seven ML models were accessed to predict the MEK active compounds using six molecular representations. The LGB model with morgan2 fingerprints surpasses other models ensuing 0.92 accuracy and 0.83 MCC value versus test set and 0.85 accuracy and 0.70 MCC value with external set. Further, the binding ability of screened hits were examined using glide XP docking and prime-MM/GBSA calculations. Note that we have utilized three ML-based scoring functions to predict the various biological properties of the compounds. The two hit compounds such as DB06920 and DB08010 resulted excellent binding mechanism with acceptable toxicity properties against MEK. Further, 200 ns of MD simulation combined with MM-GBSA/PBSA calculations confirms that DB06920 may have stable binding conformations with MEK thus step forwarded to the experimental studies in the near future.

Communicated by Ramaswamy H. Sarma

Keywords:

• Machine learning
• MEK inhibitor
• scoring function
• molecular dynamic simulation
• free energy landscape

Acknowledgment

The authors thank the Management of Vellore Institute of Technology for providing the facilities to carry out this research work.

Authors’ contributions

R.K. designed the computational framework. M.K.T. performed the computational work, prepared tables, and figures. M.K.T, S.V and R.K analyzed the data. M.K.T, S.V and R.K. contributed to the writing of the manuscript. R.K. supervised the entire study. All authors reviewed and approved the final version of the manuscript.

Data availability statement

The raw dataset used in the current study was taken from CHEMBL database accession number: CHEMBL3587 and CHEMBL5749.

Disclosure statement

The authors declare that they have no conflict of interest.

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.