Mind & Machine in Drug Design
Integrating domain-specific machine intelligence in the pharmaceutical industry is a moonshot program for AI in healthcare. The challenge is not only to generate novel drug candidates that are — practically — optimal in terms of their pharmacokinetic and -dynamic properties; the integration of AI will also require significant investment of time, money and the reorganization of laboratory structures and discovery processes. In consequence, the envisaged automated drug design engine may not only imitate but exceed human decision making as a core aspect of the drug discovery process. If successful in the long run, the approach will combine a continuously learning, chemistry-savvy AI with the synthesis and testing of pharmacologically relevant chemical matter. Academic institutions and not-for-profit organizations can offer the necessary leverage, and leeway, to explore unconventional thinking and challenge machine intelligence models to generate novel drug candidates. The RETHINK “Think-and-Do Tank” provides such an environment at ETH Zurich.
Read more about this exciting concept in the latest publication by RETHINK director Gisbert Schneider: Nature Machine Intelligence 1, 128-130 (2019).
Credit: Jack Burgess