数据链接：
官方服务：
资源简介：
Copy of the data file originally from https://zlab.umassmed.edu/benchmark/benchmark5.5.tgz
应用场景：
创建时间：
2023-09-05
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Reliable Prediction of the Protein–Ligand Binding Affinity Using a Charge Penetration Corrected AMOEBA Force Field: A Case Study of Drug Resistance Mutations in Abl Kinase
Protein mutations that directly impair drug binding are related to therapeutic resistance, and accurate prediction of their impact on drug binding would benefit drug design and clinical practice. Here
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Benchmark set.
Each row lists a test case. Columns Protein and Ligand contain the name of protein or ligand, Positions the indices of the mutable positions (for HIV protease along with the chain identifier, in the o
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Performance of TNet-BP and PATH+ on the PDBBind v2020 refined set shows that PATH+ achieves similar performance with TNet-BP while having three orders fewer features.
For each of the algorithms we have implemented, mean standard deviation is reported for root mean squared error (RMSE) and between predicted and experimental over 100 random restarts. The RMSE is comp
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MDD-Molecular Dynamics Dataset: Collection of protein-ligand complex simulations
Dataset is part of the paper: https://chemrxiv.org/engage/chemrxiv/article-details/664c73f6418a5379b0de8152. This dataset consists of molecular dynamics (MD) simulations of 862 unique protein-ligand
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Experimental results on the PDBbind core set.
Protein-ligand interactions are crucial in drug discovery. Accurately predicting protein-ligand binding affinity is essential for screening potential drugs. Graph neural networks have proven highly ef
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