Designing algorithms to aid discovery by chemical robots

Recently, automated robotic systems have become very efficient, thanks to improved coupling between sensor systems and algorithms, of which the latter have been gaining significance thanks to the increase in computing power over the past few decades. However, intelligent automated chemistry platforms for discovery orientated tasks need to be able to cope with the unknown, which is a profoundly hard problem. In this Outlook, we describe how recent advances in the design and application of algorithms, coupled with the increased amount of chemical data available, and automation and control systems may allow more productive chemical research and the development of chemical robots able to target discovery. This is shown through examples of workflow and data processing with automation and control, and through the use of both well-used and cutting-edge algorithms illustrated using recent studies in chemistry. Finally, several algorithms are presented in relation to chemical robots and chemical intelligence for knowledge discovery

Exploiting transient protein states for the design of small-molecule stabilizers of mutant p53

The destabilizing p53 cancer mutation Y220C creates an extended crevice on the surface of the protein that can be targeted by small-molecule stabilizers. Here, we identify different classes of small molecules that bind to this crevice and determine their binding modes by X-ray crystallography. These structures reveal two major conformational states of the pocket and a cryptic, transiently open hydrophobic subpocket that is modulated by Cys220. In one instance, specifically targeting this transient protein state by a pyrrole moiety resulted in a 40-fold increase in binding affinity. Molecular dynamics simulations showed that both open and closed states of this subsite were populated at comparable frequencies along the trajectories. Our data extend the framework for the design of high-affinity Y220C mutant binders for use in personalized anticancer therapy and, more generally, highlight the importance of implementing protein dynamics and hydration patterns in the drug-discovery process