Commit 2ab4d0e1 authored by Mufei Li's avatar Mufei Li Committed by VoVAllen
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[Doc] Hotfix for README (#1119)

parent 7b98e899
...@@ -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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