Designing efficient drugs for curing diseases is of essential importance for the 21st century's life science. It involves an extremely complicated procedure, including disease identification, target hypothesis, i.e., the activation or inhibition of drug targets, screening potential drugs that can effectively bind to the target, optimizing the structures of identified drugs, preclinical in vitro and in vivo tests, clinical trials to determine their bioavailability and therapeutic potential and finally optimizing drug's efficacy, toxicity, and pharmacokinetics properties. We propose differential geometry for multiscale representation of protein-drug complexes, algebraic topology for high throughput drug screening, graph theory for protein flexibility detection, partial differential equation for electrostatic steering, and their combination with machine learning techniques for the predictions of drug solubility, partition coefficient, toxicity, binding pose and protein-ligand binding affinity.

Mathematics for drug design and discovery

254th National Meeting (2017)

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.