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Computational Biophysics

Mathematical deep learning for drug discovery

A major trend of biological sciences in the 21st century is their transition from quantitative, phenomenological and descriptive to a quantitative, analytical and predictive. Fundamental challenges that hinder the current  understanding of biomolecular structure-function relationships, which is the central theme of biological sciences,  are their tremendous structural complexity and excessively large datasets. These challenges call for innovative strategies.

Modern mathematical methods, such as those based on differential geometry, algebraic topology  and graph theory, are able to provide high-level abstractions of biomolecular systems. However, these methods were rarely properly applied to the analysis of massive and diverse biomolecular datasets. The PI has recently made a paradigm-shift progress on devising modern mathematics for biomolecular data analysis. Specifically, the PI has developed algebraic topology and graph theory  based methods to win a number of contests in two recent D3R Grand Challenges, a worldwide competition series in computer-aided drug design, which ultimately tests our understanding of the biomolecular world and brings a direct benefit to human health (https://doi.org/10.1007/s10822-018-0146-6).

In the proposed project, we will integrate mathematics (algebraic topology, differential geometry and/or graph theory) and deep learning for drug design and discovery.  The objective of the present project is to develop new mathematics (such as de Rham cohomology and Hodge theory) based approaches to revolutionize the current practice in biomolecular data analysis and modeling. The proposed methods will be extensively validated on a variety of datasets,  such as protein binding to protein, ligand,  DNA and RNA, protein folding stability changes upon mutation, drug toxicity, solvation, solubility, and partition coefficient.   User-friendly software packages and online servers will be developed using parallel and GPU architectures for researchers who are not formally trained on mathematics or machine learning.

Keywords: Mathematical/computational biophysics

Mentor(s): Guo-Wei Wei, Professor in MSU's Department of Mathematics; Associate Professor, Electrical & Computer Engineering; Professor, Biochemistry & Molecular Biology