Bioactivity predictions and virtual screening using machine learning predictive model

Abstract

Recently, there has been significant attention on machine learning algorithms for predictive modeling. Prediction models for enzyme inhibitors are limited, and it is essential to account for chemical biases while developing them. The lack of repeatability in available models and chemical bias issues constrain drug discovery and development. A new prediction model for enzyme inhibitors has been developed, and the model efficacy was checked using Dipeptidyl peptidase 4 (DPP-4) inhibitors. A Python script was prepared and can be provided for personal use upon request. Among various machine learning algorithms, it was found that Random Forest offers the best accuracy. Two models were compared, one with diverse training and test data and the other with a random split. It was concluded that machine learning predictive models based on the Murcko scaffold can address chemical bias concerns. In-silico screening of the Drug Bank database identified two molecules against DPP-4, which are previously proven hit molecules. The approach was further validated through molecular docking studies and molecular dynamics simulations, demonstrating the credibility and relevance of the developed model for future investigations and potential translation into clinical applications.

Communicated by Ramaswamy H. Sarma

Keywords:

• Machine learning predictive model
• DPP-4 inhibitors
• molecular docking
• mMGBSA
• molecular dynamics simulation

Acknowledgment

The authors express their gratitude to Uresearcher.com for providing the codes.

Author contributions

NFS, PV, and ST developed the model, performed all in silico experiments, and wrote the manuscript. HRJ conceptualized, monitored, and coordinated the entire study. All authors have contributed equally.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

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