The unprecedented convergence of AI and Machine Learning in Biology with the life sciences, often described as AI in biology, represents a milestone in scientific progress. AI/ML, once the exclusive tools of computer scientists, is now being incorporated into several scientific disciplines, with biology at the forefront. At the vanguard of a new era of biomedical discovery and innovation, these technologies empower researchers to address an expanding spectrum of scientific questions, ranging from the intricate biochemical mechanisms that govern cellular functions to the urgent challenges in modern healthcare, opening new avenues for understanding the fundamental processes of life. Artificial intelligence is the broader field of developing systems capable of performing tasks generally associated with human intelligence. Machine learning is a subset of AI in which algorithms learn patterns from data and use them to generate predictions, classifications or recommendations. Table of Contents Why Is AI and Machine Learning in Biology Important for Research? Modern biology generates enormous and diverse datasets across the domains of genomics, proteomics, imaging, and clinical records. The need to process this information to extract meaningful patterns and insights often proves to be a formidable challenge, requiring substantial analytical effort by researchers. This is where the unique strengths and capabilities of AI/ML come to the forefront, enabling researchers to perform tasks such as: - Pattern Recognition in Large, Complex Datasets AI and ML algorithms are uniquely capable of detecting subtle patterns and associations that might be challenging or impossible for humans to discern, therefore often guiding the research process. - Predictive Modelling AI/ML can identify trends and patterns that can be used to build predictive models capable of rapidly analyzing complex biological systems and processes, such as disease progression or molecular interactions. These include, but are not limited to, modelling disease processes, predicting drug-target interactions, and identifying