Virtual high-throughput screening is used to find “hits” from libraries of hundreds of thousands of molecules. However, brute force techniques of enumerating over all molecules in these large libraries requires that less time is spent on calculating individual molecules’ binding affinities. This is usually done with low level binding affinity methodologies (such as docking) giving low accuracy results. On the other hand, using AI searching techniques will reduce the number of molecules to investigate, and allow using a more computationally intense (and more accurate) LIE QM/MM binding affinity algorithm.We present here quantum molecular design (QMD), a novel cost-saving automated framework for de novo computational drug design. This technology not only addresses many of the challenges faced in the computer-aided drug design field by using highly accurate QM/MM binding affinity calculations, it also dramatically lowers costs by leveraging an AI heuristic search algorithm with targeted chemical space. Application of QMD to design selective JAK3 inhibitors will also be presented.<br/>

Leveraging cloud computing for <i>in-silico</i> drug design using the Quantum Molecular Design (QMD) framework

2019 Spring National Meeting

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Leveraging cloud computing for in-silico drug design using the Quantum Molecular Design (QMD) framework