The Future of Biological Studies in Light of the AI Revolution

Over a decade ago, Eric Lander, a distinguished biologist, observed, “Biology is becoming a data science.” This foresight has proven profoundly prescient. With the exponential growth of biological data and the rapid ascent of artificial intelligence (AI), we are witnessing a transformative shift in how life is studied, understood, and engineered. This editorial explores the pivotal role of AI in redefining biology, its associated challenges, and a responsible path forward for the broader scientific community, including biologists, data scientists, and science policymakers. AI is reshaping the landscape of biological research. In genomics, advanced algorithms, such as deep learning models, integrate complex datasets—from DNA sequences to epigenetic patterns and proteomic profiles—unveiling insights previously beyond reach. For instance, recent AI-driven studies have identified novel gene-disease associations, such as new biomarkers for type 2 diabetes (Nature Genetics, 2023). In structural biology, DeepMind’s AlphaFold has resolved a decades-long grand challenge by making protein structure prediction a routine step in research pipelines. Introduced in 2020, this breakthrough has accelerated drug design and deepened our understanding of biological mechanisms. In drug discovery, AI now prioritizes therapeutic targets, designs novel molecules, and enables personalized treatments at unprecedented speeds. For example, Insilico Medicine leveraged AI to develop a potential drug for pulmonary fibrosis in under 18 months—a process that traditionally took over five years.