Optimal Transport Graph Neural Networks · Property smoothness & robustness of representations...
Transcript of Optimal Transport Graph Neural Networks · Property smoothness & robustness of representations...
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Optimal Transport Graph Neural Networks
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Graph Convolutions and Chemprop Model
Toxic?+
Molecule embedding
Graph Convolutional
Network / Chemprop
(GCN)
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Property smoothness & robustness of representations
Small, soluble Large, soluble
Large, insoluble
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Solu
bilit
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Molecule embedding distance
Pro
perty
val
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ista
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Property smoothness & robustness of representations
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From atom embeddings to molecule embedding
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• Simplistic graph compression
•
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Green, red and orange sets have the same sum and same average.
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• Simplistic graph compression
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From atom embeddings to molecule embedding
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Toxic?
Better set functions?
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From atom embeddings to molecule embedding
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Prototypes Inspired From Functional Groups
Prototype 1: Aromatic Nitro
Prototype 3: Nitroso
Prototype 2: Azo-type
Prototype 3: Aromatic Amine
dist=0.5
dist=2
dist=10
dist=20
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Our idea: • learn a dictionary of structural basis functions that serve to highlight key facets of compounds • express molecules by relating them to abstract molecular prototypes • prototypes highlight property values associated with different structural features (e.g. solubility)
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Prototypes as GCN Embedding Point Clouds
Prototype 1: Aromatic Nitro
Prototype 3: Nitroso
Prototype 2: Azo-type
Prototype 3: Aromatic Amine
dist=0.5
dist=2
dist=10
dist=20
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Our idea: • learn a dictionary of structural basis functions that serve to highlight key facets of compounds • express molecules by relating them to abstract molecular prototypes • prototypes highlight property values associated with different structural features (e.g. solubility)
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Wasserstein Prototypes
Molecule atom embeddings
Prototype 1Prototype 2
Prototype 3Prototype 4
d4
d1
d3
d2
d1d2d3d4
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Wasserstein Prototypes
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Wasserstein distances between sets of embeddings
Molecule atom embeddings
Prototype 1 Prototype 2
Prototype 3Prototype 4
d4
d1
d3
d2
d1d2d3d4
Toxic?
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Property smoothness: Latent Space for Real Models
Graph Convolution w/ sum aggregation
Spearman ⍴ = .424±.029
Pearson r = .393±.049
Wasserstein Prototypes
Spearman ⍴ = .815±.026
Pearson r = .828±.020
Molecule embedding in 2D vs property value
Molecule embedding distance vs property value distance
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Property smoothness: Latent Space for Real Models
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Property smoothness: Latent Space for Real Models
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Results on Molecular Property Prediction
Solubility LipophilicityInhibitors of human
β-secretase 1 (BACE-1)
Blood-brain barrier
penetration (permeability)
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