Open Innovation Drug Discovery (OIDD): a potential path to novel therapeutic chemical space.

The continued development of computational and synthetic methods has enabled the enumeration or preparation of a nearly endless universe of chemical structures. Nevertheless, the ability of this chemical universe to deliver small molecules that can both modulate biological targets and have drug-like physicochemical properties continues to be a topic of interest to the pharmaceutical industry and academic researchers alike. The chemical space described by public, commercial, in-house and virtual compound collections has been interrogated by multiple approaches including biochemical, cellular and virtual screening, diversity analysis, and in-silico profiling. However, current drugs and known chemical probes derived from these efforts are contained within a remarkably small volume of the predicted chemical space. Access to more diverse classes of chemical scaffolds that maintain the properties relevant for drug discovery is certainly needed to meet the increasing demands for pharmaceutical innovation. The Lilly Open Innovation Drug Discovery platform (OIDD) was designed to tackle barriers to innovation through the identification of novel molecules active in relevant disease biology models. In this article we will discuss several computational approaches towards describing novel, biologically active, drug-like chemical space and illustrate how the OIDD program may facilitate access to previously untapped molecules that may aid in the search for innovative pharmaceuticals.

Rationalizing lead optimization by associating quantitative relevance with molecular structure modification.

Historically, one of the characteristic activities of the medicinal chemist has been the iterative improvement of lead compounds until a suitable therapeutic entity is achieved. Often referred to as lead optimization, this process typically takes the form of minor structural modifications to an existing lead in an attempt to ameliorate deleterious attributes while simultaneously trying to maintain or improve desirable properties. The cumulative effect of this exercise performed over the course of several decades of pharmaceutical research by thousands of trained researchers has resulted in large collections of pharmaceutically relevant chemical structures. As far as the authors are aware, this work represents the first attempt to use that data to define a framework to quantifiably catalogue and summate this information into a medicinal chemistry expert system. A method is proposed that first comprehensively mines a compendium of chemical structures compiling the structural modifications, abridges them to rectify artificially inflated support levels, and then performs an association rule mining experiment to ascribe relative confidences to each transformation. The result is a catalogue of statistically relevant structural modifications that can potentially be used in a number of pharmaceutical applications.