Revolutionizing Molecular Design for Innovative Therapeutic Applications through Artificial Intelligence

• Published in: Molecules (Basel, Switzerland) Vol. 29; no. 19; p. 4626
• Main Authors: Son, Ahrum, Park, Jongham, Kim, Woojin, Yoon, Yoonki, Lee, Sangwoon, Park, Yongho, Kim, Hyunsoo
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.10.2024; MDPI
• Subjects: Accuracy; Artificial Intelligence; Bioinformatics; Biology; computational biology; Computational Biology - methods; Computational linguistics; de novo protein design; Deep learning; Drug Design; Drug development; Engineering; Enzymes; Health aspects; Humans; Innovations; Language processing; Machine Learning; Models, Molecular; molecular design; Natural language interfaces; Network topologies; Neural networks; Optimization techniques; Protein binding; protein engineering; Protein Engineering - methods; Protein structure prediction; Proteins; Proteins - chemistry; Review; therapeutic proteins; South Korea; computational biology; synthetic biology; protein engineering; de novo protein design; molecular design; artificial intelligence; therapeutic proteins
• ISSN: 1420-3049; 1420-3049
• DOI: 10.3390/molecules29194626

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 biotechnology and medicine. Techniques such as deep learning, reinforcement learning, and transfer learning have dramatically improved protein structure prediction, optimization of binding affinities, and enzyme design. These innovations have streamlined the process of protein engineering by allowing the rapid generation of targeted libraries, reducing experimental sampling, and enabling 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 addressing ethical concerns related to AI-driven protein design. This review provides a comprehensive 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.