Research team
Expertise
My research activities are focused on theoretical chemistry with a particular focus on the combination of state-of-the-art theoretical chemistry methods with machine learning, aiming to predict the molecular properties of large biological systems based on reactivity. I have wide expertise in modelling large biological systems using hybrid, classical and quantum methods, as well as structural chemistry methods. During my scientific career I have gained a lot of programming experience, and I enjoy coding in the HPC framework.
Conceptual DFT a novel tool for virtual screening.
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.Researcher(s)
- Promoter: Cunha Ana
- Fellow: Rostami Pour Kiana
Research team(s)
- Theory and Spectroscopy of Molecules and Materials (TSM²)
Project type(s)
- Research Project