Investigating Synergistic Strategies: Integrating Linear Regression, Quantum Mechanics, and Molecular Dynamics for the Discovery of Novel Anticancer Drugs Targeting MTH1 Inhibition.

Abstract

Introduction: Cancer remains a leading cause of mortality worldwide. Specific proteins play critical roles in cancer development, and MTH1 is one such protein. MTH1 removes the terminal phosphate groups from oxidized nucleotides like 8-oxo-dGTP and 2- OH-dATP, generated by oxidative stress in tumor cells.

Methods: These oxidized nucleotides can disrupt DNA replication and cell division. By preventing their incorporation into newly synthesized DNA, MTH1 promotes cancer cell proliferation. Developing new anticancer drugs is complex, but interdisciplinary research can significantly contribute to this endeavor. For the first time, we propose a multipronged approach utilizing computational chemistry, statistical analysis, machine learning, molecular dynamics simulations, and synthesis to design novel MTH1 inhibitors.

Results: This approach underscores the power of collaboration between diverse scientific disciplines. Our research aims to identify potent MTH1 inhibitors through a synergy of these methodologies.

Conclusion: This comprehensive study demonstrates that computational chemistry, statistical analysis, and MD simulations can be effectively integrated. Our findings from this combined approach illustrate that our newly designed MTH1 inhibitor, Xyl-Trp, can be a promising candidate for MTH1 inhibition.