ARTIFICIAL INTELLIGENCE IN BIOTECHNOLOGICAL DRUG DISCOVERY RECENT ADVANCES

Abstract

This research explores the existing state of artificial intelligence (AI) in biotechnological drug research, with an emphasis on AI's vital function in speeding and enhancing the drug development process. An increasing range of AI methods, like as data-driven modeling, deep learning, and machine learning, are being used to more quickly and accurately evaluate complex biological data, anticipate molecular interactions, find possible therapeutic targets, and enhance lead drugs. These methods greatly minimize the time, expense, and risk that are usually involved with traditional drug discovery processes. By combining genetic, proteomic, and clinical data to produce more potent and focused medications, AI also enables personalized treatment. The use of AI in biotechnology continues to transform pharmaceutical research despite obstacles related to data quality, model interpretability, and regulatory limitations. It is quickening the process of finding new drugs and treating complicated diseases.