INTEGRATING MIXED REALITY WITH NEURAL NETWORKS FOR ADVANCED MOLECULAR VISUALIZATION IN BIOINFORMATICS: A MATHEMATICAL FRAMEWORK FOR DRUG DISCOVERY
DOI:
https://doi.org/10.17770/etr2024vol2.8033Keywords:
Mixed Reality, Bioinformatics, Neural NetworksAbstract
In this study, we develop and present an innovative approach that integrates Mixed Reality (MR) technologies with neural network algorithms, aiming to revolutionize molecular structure visualization in bioinformatics through the application of mathematical methods. The development includes the creation of a mathematical framework aimed at optimizing drug discovery processes, utilizing the potential of MR to facilitate detailed and interactive exploration of molecules in three-dimensional space.
Our approach is based on the use of Unreal Engine for the realization of a simulation environment and the application of Python and PyTorch for the development of complex neural network models. These models are capable of efficiently processing and analyzing molecular data, enabling scientifically grounded manipulation of molecular structures. This approach facilitates the identification of potential active sites for interaction with pharmaceutical agents, improving the efficiency and speed of the drug discovery process.
A key aspect of our work is the development of a comprehensive mathematical framework that effectively simplifies and optimizes molecular design and analysis, while simultaneously increasing the accuracy of predictions for interactions between potential drug molecules and their targets. This approach not only enriches our understanding of the molecular basis of diseases but also offers a more rational and economical path to pharmacological development.
In conclusion, we propose a new approach that we hope will be considered and applied by the scientific community. This method presents a promising opportunity for advancement in research and development in bioinformatics and pharmacology, providing a solid foundation for further exploration of molecular dynamics and drug discovery through the application of mathematical and computer sciences.
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Copyright (c) 2024 Ivan Trenchev, Tereza Trencheva, Vladimir Angelov, Yordan Spirov, Yana Karshiyska, Kamelia Shumanova
This work is licensed under a Creative Commons Attribution 4.0 International License.
