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| Lance Westerhoff, QuantumBio |
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Lance Westerhoff is a former graduate student of Dr. Ken Merz. His PhD dissertation project includes populating the protein database with the 6,000 structures that will be licensed to QuantumBio. He is currently taking a leave of absence from his PhD to work for QuantumBio full-time. Mr. Westerhoff is an expert software developer and is directing the group that is programming the suite of datamining tools that QuantumBio will license. His combined undergraduate degrees in Computer Science and Biochemistry ideally suit him to this project.
In addition to his doctoral experience as a software development team leader, he has held highly entrepreneurial jobs including working for eight years in commission-based sales jobs at Sears, including selling Computers and selling in a variety of other departments. While in college and graduate school he has founded and operated several businesses that provide software development and web-development consulting.
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Quantum Biochemistry Workflows
Lance Westerhoff, QuantumBio
QuantumBio, Inc. in conjunction with Discovery Machine, Inc. has been working to apply computer learning and knowledge management to fully automate the multi-step processes required to characterize biomolecular interactions at a quantum mechanical level within in silico drug discovery workflows. Such workflows involve database searches, structure preparation, molecular mechanics-based cleanup, and finally quantum mechanical treatment in order to fully characterize these interactions. Each of these steps can include any number of subtasks. During the in silico drug discovery process, these complicated workflows are coupled with simulations that involve the characterization of hundreds if not thousands of biomolecular structures at a time. In addition to simulation parameters themselves, quantum mechanics methodologies are notoriously sensitive to structural defects in which the convergence of the calculation will be adversely affected. This leads to longer calculation times and other problems. Therefore, these simulations often require that the user understand not only the chemistry of the structure, but also the theory involved in the computational methodologies so that problem structures can be filtered early in the process.
With this in mind, an intelligent and adaptive system for quantum mechanics-based, in silico drug discovery has been developed to encapsulate these workflows to describe the types and strengths of enzyme-inhibitor interactions that play an important roll in drug discovery efforts. This system is built on the synergy between QuantumBio’s CHEMIX molecular modeling user interface and Discovery Machine’s workflow management solution. The goal of this workshop is to introduce the community to an early version of this system in order to demonstrate its usefulness, and to gain feedback for continued development.
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