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OpenDAS
dgl
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2ab4d0e1
Commit
2ab4d0e1
authored
Dec 23, 2019
by
Mufei Li
Committed by
VoVAllen
Dec 23, 2019
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[Doc] Hotfix for README (#1119)
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examples/pytorch/model_zoo/chem/binding_affinity_prediction/README.md
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2ab4d0e1
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@@ -49,9 +49,14 @@ roughly 3.3 for training time per epoch (from 1.40s to 0.42s). If we do not care
...
@@ -49,9 +49,14 @@ roughly 3.3 for training time per epoch (from 1.40s to 0.42s). If we do not care
randomness introduced by some kernel optimization, we can achieve a speedup by roughly 4.4 (from 1.40s to 0.32s).
randomness introduced by some kernel optimization, we can achieve a speedup by roughly 4.4 (from 1.40s to 0.32s).
## References
## References
[1] Wu et al. (2017) MoleculeNet: a benchmark for molecular machine learning.
*Chemical Science*
9, 513-530.
[1] Wu et al. (2017) MoleculeNet: a benchmark for molecular machine learning.
*Chemical Science*
9, 513-530.
[2] Wang et al. (2004) The PDBbind database: collection of binding affinities for protein-ligand complexes
[2] Wang et al. (2004) The PDBbind database: collection of binding affinities for protein-ligand complexes
with known three-dimensional structures.
*J Med Chem*
3;47(12):2977-80.
with known three-dimensional structures.
*J Med Chem*
3;47(12):2977-80.
[3] Wang et al. (2005) The PDBbind database: methodologies and updates.
*J Med Chem*
16;48(12):4111-9.
[3] Wang et al. (2005) The PDBbind database: methodologies and updates.
*J Med Chem*
16;48(12):4111-9.
[4] Liu et al. (2015) PDB-wide collection of binding data: current status of the PDBbind database.
*Bioinformatics*
1;31(3):405-12.
[4] Liu et al. (2015) PDB-wide collection of binding data: current status of the PDBbind database.
*Bioinformatics*
1;31(3):405-12.
[5] Gomes et al. (2017) Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity.
*arXiv preprint arXiv:1703.10603*
.
[5] Gomes et al. (2017) Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity.
*arXiv preprint arXiv:1703.10603*
.
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