title: Open Innovation Drug Discovery (OIDD): a potential path to novel therapeutic chemical space.
abstract: 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.
authors.forename: Maria
authors.initials: M
authors.lastname: Alvim-Gaston
authors.forename: Timothy
authors.initials: T
authors.lastname: Grese
authors.forename: Abdelaziz
authors.initials: A
authors.lastname: Mahoui
authors.forename: Alan D
authors.initials: AD
authors.lastname: Palkowitz
authors.forename: Marta
authors.initials: M
authors.lastname: Pineiro-Nunez
authors.affiliation: Eli Lilly & Company, Lilly Corporate Center, Indianapolis, IN 46285 U.S.A. alvimgaston@lilly.com.
authors.forename: Ian
authors.initials: I
authors.lastname: Watson
mesh_terms: D019295:Computational Biology; D055808:Drug Discovery / Q000379:methods*; D006801:Humans