Accelerating molecular optimization with AI

Many of today's most urgent problems demand new molecules and materials, from antimicrobial drugs to fight superbugs and antivirals to treat novel pandemics to more sustainable photosensitive coatings for semiconductors and next-generation polymers to capture carbon dioxide right at its source. We can design these from scratch, using AI to expedite the otherwise expensive and slow process, or we can tweak existing molecules to fine-tune the properties we care about -- such as toxicity, activity, or stability. Starting from a known molecule is like getting a head start on the design and production of candidate molecules, as we know they have some of the characteristics we need, and we can use existing knowledge and manufacturing pipelines to synthesize and test them down the line. The challenge in this process, called molecular optimization, is that tweaking an existing molecule can produce a huge number of variants. They won't all have the desired properties, and evaluating them empirically to find those that do would take too much time and money to be feasible.