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Abstract

PROPHECY OF INDOLIZINE CONTAINING C200-3231 USING MOLECULAR DYNAMIC SIMULATIONS & MACHINE LEARNING METHODS AGAINST CHRONIC MYELOID LEUKEMIA DISEASE

Sangeeta Mahaur*, Sukirti Upadhyay and Jitendra

Abstract

An abnormal and expeditious facsimile of oncogene bcr-abl protein in the stem cells has been recognized as the foremost cause of Chronic Myeloid Leukemia. It has been renowned that tyrosine kinase jurisdiction of bcr-abl protein is a probable remedial target for the accomplishment of Chronic Myeloid Leukemia. The persistence of the contemporaneous revision of the target and furthermost optimized hit complex stability was explored by molecular dynamics. machine learning-based computational predictive models based on molecular dynamics trajectories for training machine learning models still need to be explored. In the current analysis, parameters have been extricated from the molecular dynamics approaches of the C200-3231 complex to train paradigm operate quantum chemical energies and forces for accuracy and efficiency, long-ranged interactions, quantum kinetics, transferability of coarse-grained models, the kinetics of coarse-grained models, transferable prediction of intensive properties, equivariant generative networks with parameter sharing, explainable artificial intelligence methods or Deep tensor neural network, schNet and VAMPnet. C200-3231 complex was docked using the glide unit of Schrodinger software, and rapid molecular dynamics simulations of 10 ns were achieved for the cunning of molecular dynamics parameters. This research recognized that molecular dynamics parameters could be excellently used for the description of the C200-3231 complex accompanied by lead optimization.

Keywords: CML, Indolizine, Machine learning, Molecular dynamic simulations, Quantum mechanics/Molecular mechanics.


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