Abstract
Computational chemistry methods have proven to be essential tools for drug development. In this research proposal we plan to develop DFT based methods combined with ML, that can accurately predict the activity of drugs in binding sites of viral proteases, by using reactivity indexes. The main goal is to push the limit of virtual screening forward, by introducing DFT reactivity descriptors that can classify possible drug candidates more accurately. Thus, greatly reducing the number of false positives. Therefore, we will use conceptual DFT methods (i.e., reactivity descriptors) to train a neural network that can predict with high accuracy the reactivity between a drug and an active site. This will be first applied to three classes of protease (Serine, Aspartyl, Cysteine) proteins, and later expanded to mutated active sites.
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