ABSTRACT The estimates of natural product producing gene clusters within the genomes of microorganisms are far greater than the number of known natural products, indicating that there is a wealth of untapped molecules within these organisms that offer huge potential as leads for new medicines. Unfortunately, the prevailing paradigms for natural product discovery are ill suited for accessing this bounty of molecules, providing a rate of discovery that is unacceptable in the modern era. There is, therefore, a significant gap between the potential of microorganisms to deliver new natural products and our ability to access this potential effectively. To bridge this gap, Microbial Pharmaceuticals is developing the next-generation technology that will deliver unparalleled access to these compounds and the means by which they can be characterized and put to use. The long-term goal of Microbial Pharmaceuticals is to provide a complete roadmap of secondary metabolism within actinobacteria?known to produce the greatest numbers of natural products?by conducting metabologenomic screening of 10,000 different strains. The data generated from this process will provide all expressed metabolites and their coupled biosynthetic gene clusters, while the large scale of this effort will overcome the ?cryptic? gene cluster problem faced by approaches that only investigate a few strains. Microbial Pharmaceuticals will optimize our metabologenomics data acquisition platform (Aim 1) and simultaneously improve upon our correlation algorithms (Aim 2). Optimization of the platform to allow rapid and reproducible data acquisition will be achieved using UPLC and high-mass accuracy mass spectrometry with the goal of decreasing cost and increasing the rate of acquisition (Aim 1a). This process will be piloted on 50 new bacterial strains and the data incorporated into a database of known and new natural products (Aim 1b). The successful outcome of Aim 1 during Phase I will lay the significant groundwork to achieve our long-term future goal of screening 10,000 strains cheaply and efficiently. We will also implement an MS2 networking feature into the metabologenomics database (Aim 2). The combination of metabologenomic data from the 50 strains investigated, coupled to the improved correlations, will provide a plethora of new molecules that may be accessed in a deterministic fashion. By operating in a fundamentally new modus operandi, a steady stream of high value compounds will emerge, which have been honed by nature over millennia for activity in complex biological systems.