Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges.
Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges.
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DOI：
10.1007/s10822-018-0146-6
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发表时间：
2019-01
影响因子：
3.5
通讯作者：
Wei GW
中科院分区：
文献类型：
--
作者：
Nguyen DD;Cang Z;Wu K;Wang M;Cao Y;Wei GW
高级数学，例如多尺度加权的彩色子图和特定元素的持久同源性，包括深神经网络（包括深神经网络）的机器学习构建了用于姿势和结合亲和力预测的数学深度学习模型，并在最后两个D3R的挑战中构建了计算机辅助的药物挑战2的D3R大挑战。我们的模型在第2阶段的自由能预测中获得了最高的竞争，即D3R Grand Challenge 3（GC3）是迄今为止最困难的挑战。我们的预测在这26个任务中的10个中排名第一。
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.
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影响因子：
2.9
作者：
Jacobson, MP;Pincus, DL;Friesner, RA
通讯作者：
Friesner, RA
DOI：
10.1002/cnm.2914
发表时间：
2018-02-01
影响因子：
2.1
作者：
Cang, Zixuan;Wei, Guo-Wei
通讯作者：
Wei, Guo-Wei
影响因子：
5.6
作者：
Baum, Bernhard;Muley, Laveena;Klebe, Gerhard
通讯作者：
Klebe, Gerhard
影响因子：
3.5
作者：
Dixon, Steven L.;Smondyrev, Alexander M.;Friesner, Richard A.
通讯作者：
Friesner, Richard A.
影响因子：
3
作者：
Huang, Sheng-You;Zou, Xiaoqin
通讯作者：
Zou, Xiaoqin