Project Abstract The College of Chemistry at the University of California, Berkeley seeks funds to purchase a high performance GPU Computing Cluster with integrated petabyte scale data arrays dedicated to the current and future needs of a broad community of NIH- supported research groups who are engaged in public health driven molecular science. This equipment will be integrated into a College of Chemistry core computing facility which itself is part of a larger suite of Interdepartmental core facilities supporting NIH investigators. This proposal will enable research innovation by putting state-of-the-art computational resources into the hands of all NIH investigators, not just computational specialists. Computer modeling of chemical structure and reactivity is a key experiment design and analysis tool. Many experimental scientists collaborate with computational chemists and this is an important partnership for specialized cases. However, experimentalists can and should be enabled to do much of this everyday computational work themselves, shortening the turnaround time to potentially transformative insights that impact human health. Experimental scientists can explore critical aspects of structure and electronics, protein-ligand and protein-protein binding, bioinformatics and many other properties of molecular science. Desktop software is an entry point to computation but true insight into complex molecular science still requires supercomputing level power. Unfortunately, most supercomputing comes with a bare command line and a very high barrier to entry due to the learning curve of arcane queuing and operating systems. The College of Chemistry at UCB has a proven record of lowering the barrier to entry by training experimental scientists in computational techniques in our core facility and empowering them with software, hardware and consulting tailored to their research needs. Our NIH funded researchers are currently using a >5 year old CPU cluster which is lacking the GPU resources needed to tackle next generation transformative public health research. This proposal will fund the GPU computing power required to serve a large group of NIH supported investigators and will leverage an existing CPU cluster and a core facility staffed by professional scientists with more than 30 years of experience in computation. Expanding access to computational tools for experimental scientists is potentially transformative in terms of public health outcomes driven by molecular science.