Abstract: Our work addresses both the fundamental biological mechanisms of the SARS-CoV-2 virus and the disease, while simultaneously targeting the entire viral proteome to identify potential therapeutics. We have been developing machine learning (ML), deep learning (DL) and artificial intelligence (AI) techniques to accelerate adaptive conformational sampling of the viral proteins to potentially identify novel binding sites/ pockets that can be targeted by compound libraries and to rapidly filter, rank, and search for small molecules across widely available chemical libraries and to integrate virtual screening with experimental high throughput screening. The immediate impact of our current research is an ecosystem of open source AI/ML tools and conventional physics-based simulations that can accelerate timely response for treating such pandemics. We have released of over 60 terabytes of machine readable data from various open-source chemical compound libraries (https://2019-ncovgroup.github.io/data/), developed AI-accelerated molecular dynamics (MD) simulations to quantify the stability and binding of AI-predicted compounds across various viral targets. The outputs from physics-based models are used iteratively to improve the prediction capabilities of our AI/ML approaches, thus successively improving the overall yield of drug candidates that can be refined further using biochemical and biological assays. Together, our integrated approach provides insights into how AI-driven physics-based models can be used to accelerate drug-design processes for emerging pandemics.
AI & HPC Seminar