Abstract

Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are transforming the landscape of biological and biomedical research. These cutting-edge technologies are enabling faster, more accurate data interpretation, drug discovery, and personalized treatment approaches. AI/ML/DL tools can efficiently analyze large-scale datasets from genomics, proteomics, and imaging, while also supporting predictive modeling for protein structure, drug efficacy, and gene editing. From enhancing precision medicine to enabling advances in synthetic biology and CRISPR technologies, AI-driven approaches are becoming integral to innovation in life sciences. Despite their immense potential, challenges related to data quality, interpretability, and ethical concerns remain. This paper highlights key applications, current challenges, and future directions, emphasizing the need for integration with experimental biology and the development of a skilled workforce to fully leverage these tools in biological research.

Keywords

artificial intelligence, machine learning, deep learning, drug discovery, bioinformatics, genomics, proteomics, synthetic biology, systems biology, computational biology