Artificial Intelligence and Machine Learning in Bioinformatics

Artificial intelligence (AI) and machine learning (ML) have emerged over the past decade as the cutting-edge technologies most expected to revolutionize the research and development sector. This is fueled in part by game-changing developments in computer technology and the concomitant evaporation of barriers to collecting massive amounts of data. Meanwhile, the cost of researching, testing, manufacturing, and distributing new pharmaceuticals has risen. In light of these challenges, the pharmaceutical industry is interested in AI/ML methods because to their automation, predictability, and the ensuing anticipated boost in efficiency. The use of ML techniques in the pharmaceutical industry has matured during the past 15 years. Clinical trial design, management, and analysis are the most recent drug development process steps to benefit from AI and ML. As we move toward a world in which AI/ML is increasingly integrated into R&D, it is essential to sort through the corresponding jargon and hype. Equally crucial is the understanding that the scientific method is still relevant for drawing conclusions from evidence. By doing so, we can better evaluate the potential benefits of AI/ML in the pharmaceutical industry and make well-informed decisions on their best application. The purpose of this paper is to clarify certain fundamental ideas, provide some examples of their application, and then provide some helpful perspective on how to best apply AI/ML techniques to research and development.