Pharmaceutical drug discovery is time-consuming and expensive, with each new drug brought to market now costing well over $1 billion on average. This cost is driven by the dif?culty of drug discovery, and in part by the amount of trial and error involved in the process of ?nding initial ?hits? which modulate the function of a biomolecule, and then re?ning these into ?leads? which have adequate af?nity for the biomolecular target and other desirable properties. Here, we develop and improve computational methods to guide this process, allowing the potential ef?cacy of prospective leads to be tested computationally prior to their creation ? dramatically reducing the amount of trial and error involved in the process and guiding the molecular design process. Here, we build on previous work in the group and the ?eld on alchemical free energy calculations based on molecular simulations ? the most promising present computational technique for guiding drug discovery. However, such techniques work well only for a limited subset of cases, require considerable expertise to employ, and even their limitations are not yet well understood. Here, we focus on expanding the range of systems which can be treated with these techniques, making the calculations more robust and rapid, improving accuracy, and identifying and isolating remaining de?ciencies for repair. Alchemical free energy calculations hold particular promise both because of their accuracy and physical real- ism. Here, we focus on technology and applications of these calculations, focusing on (1) improved ef?ciency and accuracy of binding free energy calculations; (2) automation and large-scale benchmarking of free energy calculations to guide work to ensure robustness and accuracy; and (3) applications to utilizing simulations and free energy calculations to guide lead discovery and optimization of SUMO E-1 inhibitors as potential anti-cancer drugs. Broadly, Aims 1-2 focus on iteratively improving and testing computational tools, whereas Aim 3 focuses on a speci?c application with experimental collaborators. This work promises more accurate and more rapid free energy calculations, with broader scope so that they can reliably be applied to molecular design problems in drug discovery and elsewhere. Our long-term work aims to produce a work?ow where a chemist developing new molecules to bind a particular target could input hundreds of potential compounds to synthesize next into a computer before leaving work one day, and return to work the following morning to ?nd these compounds automatically prioritized based on predicted target af?nity, selectivity, solubility and other properties, allowing years worth of synthesis and assays to be bypassed. Here, we develop, test and apply technologies to help make this work?ow possible, building on our extensive previous success in physical modeling for binding prediction. This also leverages and extends technologies built in our prior R01.
Pharmaceutical drug discovery produces dramatic public health bene?ts through new and improved treatments for common diseases and disorders, but it is an expensive, time-consuming process involving much trial and error, and failure is common. This work focuses on dramatically improving computer tools to predict interactions between small molecules and molecular machines, and applies these tools to an anti-cancer drug discovery project. The proposed project produces and improves tools which will guide development of new drugs, and will directly use these to assist in anti-cancer drug discovery; both the tools and the proposed application provide potential public health rewards.
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