Development of the Chemalytics Platform for Drug Discovery

presentation · 3 years ago
by Gerald Wyckoff (Chemalytics)
Calculator Plugins (logP logD pKa etc...) JChem for Office Marvin JS

Structure-based virtual screening is an important tool in the drug discovery process. The use of computational tools has allowed for the screening of large libraries of chemical compounds to identify putative ligand-receptor interactions. The identification of valid targets and therapeutic compounds has long-term importance both to public health and the economic strength of the pharmaceutical industry. Receptor-based virtual screening (VS) is a technique in which computational tools are used dock small molecular weight compounds into a protein receptor or enzyme. This technique is most often used in drug discovery, where a large library of chemical structure can be docked and scored to assess the potential if a compound to bind to a drug target. However, high-throughput virtual screening is computationally intensive, and the cost of building, maintaining, and managing a dedicated computing cluster limits access to these technologies to large universities and commercial enterprises. Internet-based, or “cloud” computing, is a business service model in which computational resources are accessed affordably, scalably, and securely as needed. Our product utilizes this cloud infrastructure to deliver virtual screening to clients who either don’t desire to or cannot maintain their own infrastructure. Our elegant and highly efficient system for managing the job queue and maximizing the efficient use of computational resources allows us to provide reduced-cost access to our tools for academic and government researchers. This confluence of residual processing power and need has given rise to our concept of the “bucket list”; a “free” job queue that unassigned agents can perform during the time between finishing a paid job and their “death” at the end of their provisioned hour. We are working with Chemaxon to expand the capabilities of the current system through the following technical achievements: (1) integration of additional chemical libraries and library filtering tools to focus search space prior to docking; (2) enhancement of end user ability to evaluate results through integration of data analysis and visualization tools; (3) integration of additional licensed, proprietary, and public domain tools for additional functionality. This work is funded by NIH's National Institute of General Medical Science through SBIR Phase II grant GM097902

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