Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges.
Advanced mathematics, such as multiscale weighted colored subgraph and element specific persistent homology, and machine learning including deep neural networks were integrated to construct mathematical deep learning models for pose and binding affinity prediction and ranking in the last two D3R Grand Challenges in computer-aided drug design and discovery. D3R Grand Challenge 2 focused on the pose prediction, binding affinity ranking and free energy prediction for Farnesoid X receptor ligands. Our models obtained the top place in absolute free energy prediction for free energy set 1 in stage 2. The latest competition, D3R Grand Challenge 3 (GC3), is considered as the most difficult challenge so far. It has five subchallenges involving Cathepsin S and five other kinase targets, namely VEGFR2, JAK2, p38-α, TIE2, and ABL1. There is a total of 26 official competitive tasks for GC3. Our predictions were ranked 1st in 10 out of these 26 tasks.
['Nguyen|Duc Duy|DD|', 'Cang|Zixuan|Z|', 'Wu|Kedi|K|', 'Wang|Menglun|M|', 'Cao|Yin|Y|', 'Wei|Guo-Wei|GW|']
D001665:Binding Sites D002403:Cathepsins D017076:Computer-Aided Design D018360:Crystallography, X-Ray D030562:Databases, Protein D000077321:Deep Learning D015195:Drug Design D008024:Ligands D062105:Molecular Docking Simulation D011485:Protein Binding D011487:Protein Conformation D011494:Protein Kinases D018160:Receptors, Cytoplasmic and Nuclear D013816:Thermodynamics
Binding affinity Machine learning Drug design Pose prediction Algebraic topology Graph theory