The use of artificial intelligence (AI) to aid in drug discovery is just getting started, but it promises to have a big impact. Although machine learning for drug research is not brand new, progress has sped up recently. This is especially true in helping understand and simulate proteins using models like AlphaFold2. But this is only the beginning, with huge opportunities to apply deep learning to core problems in chemistry and biology.
Drug discovery occurs in the microscopic molecular world we can’t see. Here, molecules interact in complex networks of biological machines. The complexity makes these systems near impossible to describe with traditional math and physics approaches. This is where AI and machine learning can shine, by finding patterns in data to create models of biological systems. But to work well, we need AI models tailored to capture the intricate behavior of molecules, not simple statistical models.
By training AI models to represent the molecular world, we can create adjustable digital versions of these hidden systems. This gives us a platform to test ideas and search the landscape of molecules and disease to design new treatments, like DeepMind’s AlphaGo algorithm intelligently searched game scenarios. In this way, AI algorithms can explore new molecules alongside chemists and biologists.
While showing early promise, challenges remain. A key bottleneck is data – we need more high-quality molecular and biological datasets. We also need models that accurately capture complex molecular interactions. Simply borrowing techniques from computer vision and language processing is not enough. Better integration is also needed between AI, lab experiments and robotics to rapidly test predictions.
Nonetheless, AI has already shown valuable abilities, speeding up lead discovery, optimization, and virtual molecular screens. Companies like BenevolentAI and Exscientia have used AI to accelerate timelines. With responsible collaboration across fields, AI technologies could provide invaluable tools to combat disease and enable data-driven, personalized medicine. The full potential is yet to be seen.