Version 1
: Received: 31 August 2024 / Approved: 31 August 2024 / Online: 2 September 2024 (09:12:26 CEST)
How to cite:
Son, A.; Park, J.; Kim, W.; Yoon, Y.; Lee, S.; Park, Y.; Kim, H. Revolutionizing Molecular Design for Innovative Therapeutic Applications through Artificial Intelligence. Preprints2024, 2024090013. https://doi.org/10.20944/preprints202409.0013.v1
Son, A.; Park, J.; Kim, W.; Yoon, Y.; Lee, S.; Park, Y.; Kim, H. Revolutionizing Molecular Design for Innovative Therapeutic Applications through Artificial Intelligence. Preprints 2024, 2024090013. https://doi.org/10.20944/preprints202409.0013.v1
Son, A.; Park, J.; Kim, W.; Yoon, Y.; Lee, S.; Park, Y.; Kim, H. Revolutionizing Molecular Design for Innovative Therapeutic Applications through Artificial Intelligence. Preprints2024, 2024090013. https://doi.org/10.20944/preprints202409.0013.v1
APA Style
Son, A., Park, J., Kim, W., Yoon, Y., Lee, S., Park, Y., & Kim, H. (2024). Revolutionizing Molecular Design for Innovative Therapeutic Applications through Artificial Intelligence. Preprints. https://doi.org/10.20944/preprints202409.0013.v1
Chicago/Turabian Style
Son, A., Yongho Park and Hyunsoo Kim. 2024 "Revolutionizing Molecular Design for Innovative Therapeutic Applications through Artificial Intelligence" Preprints. https://doi.org/10.20944/preprints202409.0013.v1
Abstract
The field of computational protein engineering has been transformed by recent advancements in machine learning, artificial intelligence, and molecular modeling, enabling the design of proteins with unprecedented precision and functionality. Computational methods now play a crucial role in enhancing the stability, activity, and specificity of proteins for diverse applications in bio-technology and medicine. Techniques such as deep learning, reinforcement learning, and transfer learning have dramatically improved protein structure prediction, optimization of binding affin-ities, and enzyme design. These innovations have streamlined the process of protein engineering by allowing the rapid generation of targeted libraries, reducing experimental sampling, and en-abling the rational design of proteins with tailored properties. Furthermore, the integration of computational approaches with high-throughput experimental techniques has facilitated the development of multifunctional proteins and novel therapeutics. However, challenges remain in bridging the gap between computational predictions and experimental validation, and in ad-dressing ethical concerns related to AI-driven protein design. This review provides a compre-hensive overview of the current state and future directions of computational methods in protein engineering, emphasizing their transformative potential in creating next-generation biologics and advancing synthetic biology.
Keywords
Computational Biology; Protein Engineering; Artificial Intelligence; Molecular Design; De Novo Protein Design; Therapeutic Proteins; Synthetic Biology
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
