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Conformation-Guided Molecular Representation with Hamiltonian Neural Networks

ICLR 2021 · 3D vision › Face and shape · approach: Learned mesh and geometric representations · 5 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use HamNet fingerprints instead of the strongest listed baselineHamNet · QM9Multi-MAE: 1.292 -> 1.194Helped
Use HamNet fingerprints instead of the strongest listed baselineHamNet · Tox21Multi-ROC: 0.857 -> 0.875Helped
Use HamNet fingerprints instead of the strongest listed baselineHamNet · LipopRMSE: 0.566 -> 0.557Helped
Use HamNet fingerprints instead of the strongest listed baselineHamNet · FreeSolvRMSE: 0.736 -> 0.731Helped
Use HamNet fingerprints instead of the strongest listed baselineHamNet · ESOLRMSE: 0.505 -> 0.504Helped
Add conformation-related components to the Fingerprint Generator (ablation)HamNet · QM9Multi-MAE: 1.237±0.030 -> 1.194±0.038Helped
Add conformation-related components to the Fingerprint Generator (ablation)HamNet · Tox21Multi-ROC: 0.868±0.012 -> 0.875± 0.006Helped
Add conformation-related components to the Fingerprint Generator (ablation)HamNet · LipopRMSE: 0.572±0.011 -> 0.557±0.014Helped
Add conformation-related components to the Fingerprint Generator (ablation)HamNet · FreeSolvRMSE: 0.840±0.023 -> 0.731±0.024Helped
Add conformation-related components to the Fingerprint Generator (ablation)HamNet · ESOLRMSE: 0.547±0.015 -> 0.504±0.016Helped
Replace real conformations with Hamiltonian Engine-generated conformations (ablation)HamNet · QM9Multi-MAE: 1.199±0.017 -> 1.194±0.038Helped
Replace real conformations with Hamiltonian Engine-generated conformations (ablation)HamNet · Tox21Multi-ROC: 0.864±0.006 -> 0.875± 0.006Helped

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