Author: Duc Duy Nguyen; Kaifu Gao; Jiahui Chen; Rui Wang; Guo-Wei Wei
Title: Potentially highly potent drugs for 2019-nCoV Document date: 2020_2_13
ID: g5wpa2ee_4
Snippet: Recently, we have developed low-dimensional mathematical representations [9, 10] to reduce the structural complexity of macromolecules based on abstract mathematics, such as algebraic topology [17] [18] [19] [20] , differential geometry, and spectral graph theory [10, 21] . We exploit these representations to extract critical chemical and biological information for protein-ligand pose selection, binding affinity ranking, prediction, ranking, scor.....
Document: Recently, we have developed low-dimensional mathematical representations [9, 10] to reduce the structural complexity of macromolecules based on abstract mathematics, such as algebraic topology [17] [18] [19] [20] , differential geometry, and spectral graph theory [10, 21] . We exploit these representations to extract critical chemical and biological information for protein-ligand pose selection, binding affinity ranking, prediction, ranking, scoring, and screening [9, 10] . Paired with various machine learning, including deep algorithms, these approaches are the top competitor for D3R Grand Challenges, a worldwide competition series in computer-aided drug design in the past few years [22, 23] .
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