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MOESM3 of DASPfind: new efficient method to predict drugâtarget interactions

NIAID Data Ecosystem2026-03-09 收录

数据链接：

https://figshare.com/articles/dataset/MOESM3_of_DASPfind_new_efficient_method_to_predict_drug_target_interactions/4674130 数据链接链接失效反馈

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资源简介：

Additional file 3: DATA_2. This contains HGBI data set.

应用场景：

创建时间：

2017-02-21

药物-靶点相互作用

药物重定位

Identifying interactions between known drugs and targets is a major challenge in drug repositioning. In silico prediction of drug–target interaction (DTI) can speed up the expensive and time-consuming

Figshare2017-03-13 更新60

additional data

药物-靶点相互作用

化学信息学

df.parquet.gzip is processed raw data. This has some genes but is not fully overwrapped with training/test data drug2actions.csv is processd data that has information about drugs' effectiveness. drug2

Zenodo2023-11-09 更新30

MM-GBSA binding energy calculation of bonded and non-bonded interactions of CID_5280445 with 1z8r after every 10 ns from MD Simulation trajectories.

分子对接

药物-靶点相互作用

MM-GBSA binding energy calculation of bonded and non-bonded interactions of CID_5280445 with 1z8r after every 10 ns from MD Simulation trajectories.

Figshare2023-08-30 更新20

Insight into the Role of Active Site Protonation States and Water Molecules in the Catalytic Inhibition of DPP4 by Vildagliptin

酶催化模拟

药物-靶点相互作用

Vildagliptin (VIL) is an antidiabetic drug that inhibits dipeptidyl peptidase-4 (DPP4) through a covalent mechanism. The molecular bases for this inhibitory process have been addressed experimentally

NIAID Data Ecosystem60

Residues present around the 4 Å distances of DAMA-colchicine in tubulin 1SA0 and tubulin isotypes after simulation.

药物-靶点相互作用

分子动力学模拟

Residues present around the 4 Å distances of DAMA-colchicine in tubulin 1SA0 and tubulin isotypes after simulation.

NIAID Data Ecosystem30