Abstract

Artificial intelligence (AI) has revolutionized numerous domains, and its impact on biology, bioinformatics, and protein structure analysis is particularly remarkable. This review explores the wide-ranging applications of AI in these fields, focusing on notable examples such as ChatGPT and AlphaFold. We delve into the historical background of AI, the concept of deep learning, and its relationship to ChatGPT and other language models. Furthermore, highlight the invaluable role of ChatGPT in assisting customers, generating content, enhancing educational experiences, and aiding healthcare professionals.

In the field of biology and bioinformatics, AI technologies, including ChatGPT, have become indispensable. They facilitate the analysis of biological data, prediction of protein structures, exploration of gene expression patterns, and identification of drug targets. With advancements in natural language processing, ChatGPT promotes collaboration and knowledge sharing among scientists, enhancing communication in complex biological research.

One exemplary AI technology that has garnered significant attention is AlphaFold. Developed by DeepMind, AlphaFold leverages deep learning algorithms to predict protein structures with unprecedented accuracy. Its potential applications in drug discovery, protein engineering, and disease understanding are immense. AlphaFold's ability to rapidly and accurately determine protein structures marks a significant milestone in scientific research.

This review highlights the transformative power of AI in biology and underscores the need for continued research and development. As AI technologies continue to evolve, there is immense potential for advancements in understanding biological systems, accelerating drug discovery, and shaping the future of personalized medicine. The integration of AI, exemplified by ChatGPT and AlphaFold, offers a promising pathway for scientific progress and innovation in the biological sciences.

Key words

Artificial intelligence, ChatGPT, Transformer architecture, Deep learning, AlphaFold, Bioinformatics, Protein structure prediction