QuantumBio 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. 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 is being developed to encapsulate these workflows to describe the types and strengths of enzyme-inhibitor interactions that play an important roll in drug discovery efforts. The goal of this discussion will be to introduce the community to an early version of this system in order to demonstrate its usefulness, and to gain feedback for continued development.

Please see the eCheminfo website for more information.