about
Macromolecular target prediction by self-organizing feature maps.Performance of Deep and Shallow Neural Networks, the Universal Approximation Theorem, Activity Cliffs, and QSAR.Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets.Generative Recurrent Networks for De Novo Drug Design.Ahead of Our Time: Collaboration in Modeling Then and Now.Comparing and Validating Machine Learning Models for Mycobacterium tuberculosis Drug Discovery.Novel applications of Machine Learning in cheminformatics
P2860
Q36229691-2935193E-976D-4767-A66A-BC72D3127675Q39247822-4094B483-E890-405B-9DD2-019C6A1EAD57Q45944238-045769FE-B86C-4B57-906D-46EA29126AB9Q45944255-9FDDBA6A-47FF-4DCC-8D9C-B61365BA5C05Q50555303-AEBBCC74-789E-4C4C-AC38-9CFF70CAE15CQ52578227-DA167224-6D0B-49A2-B490-211E412DB870Q58762048-D5C209E5-FC0B-4BF8-BEBC-A42003A29CD9
P2860
description
2016 nî lūn-bûn
@nan
2016年の論文
@ja
2016年論文
@yue
2016年論文
@zh-hant
2016年論文
@zh-hk
2016年論文
@zh-mo
2016年論文
@zh-tw
2016年论文
@wuu
2016年论文
@zh
2016年论文
@zh-cn
name
The Next Era: Deep Learning in Pharmaceutical Research.
@en
type
label
The Next Era: Deep Learning in Pharmaceutical Research.
@en
prefLabel
The Next Era: Deep Learning in Pharmaceutical Research.
@en
P2860
P1476
The Next Era: Deep Learning in Pharmaceutical Research.
@en
P2860
P304
P356
10.1007/S11095-016-2029-7
P50
P577
2016-09-06T00:00:00Z