Predicting drug-drug interactions through drug structural similarities and interaction networks incorporating pharmacokinetics and pharmacodynamics knowledge.
about
Predicting potential drug-drug interactions on topological and semantic similarity features using statistical learning.Modeling polypharmacy side effects with graph convolutional networks.Mechanisms and the clinical relevance of complex drug-drug interactionsSystem Pharmacology-Based Strategy to Decode the Synergistic Mechanism of Zhi-zhu Wan for Functional Dyspepsia
P2860
Predicting drug-drug interactions through drug structural similarities and interaction networks incorporating pharmacokinetics and pharmacodynamics knowledge.
description
2017 nî lūn-bûn
@nan
2017年の論文
@ja
2017年学术文章
@wuu
2017年学术文章
@zh-cn
2017年学术文章
@zh-hans
2017年学术文章
@zh-my
2017年学术文章
@zh-sg
2017年學術文章
@yue
2017年學術文章
@zh
2017年學術文章
@zh-hant
name
Predicting drug-drug interacti ...... nd pharmacodynamics knowledge.
@ast
Predicting drug-drug interacti ...... nd pharmacodynamics knowledge.
@en
type
label
Predicting drug-drug interacti ...... nd pharmacodynamics knowledge.
@ast
Predicting drug-drug interacti ...... nd pharmacodynamics knowledge.
@en
prefLabel
Predicting drug-drug interacti ...... nd pharmacodynamics knowledge.
@ast
Predicting drug-drug interacti ...... nd pharmacodynamics knowledge.
@en
P2093
P2860
P1476
Predicting drug-drug interacti ...... nd pharmacodynamics knowledge.
@en
P2093
Stephen H Bryant
Takako Takeda
Tiejun Cheng
Yanli Wang
P2860
P2888
P356
10.1186/S13321-017-0200-8
P50
P577
2017-03-07T00:00:00Z
P6179
1084252448