New AI technology sheds light on drug development

Will artificial intelligence (AI) change the traditional trial–and–error drug development process and become a revolutionary force in the pharmaceutical sector? Active learning and interpretable AI are the two critical paradigms that lead to the positive answer, according to a perspective article recently published in Health Data Science, a Science Partner Journal. "Promising progress has been made in using AI for drug design recently. However, we are still far from certain that these early results could be translated to more effective drugs with a high success rate," said co-author Jianzhu Ma, Ph.D., a specialist and associate professor of artificial intelligence at Peking University. "How to harness the value of data is the key to building successful AI for drug development." The authors pointed out that the major limitation of conventional AI-aided drug development is its linear paradigm. Without continuous feedback from the downstream experimental results, the preceding step of AI model prediction is only "educated guesses".