Sold by: NeopolyÂ
Overview
The Neopoly Molecule Algorithm trains a Hierarchical Graph Network (HGN) with multiple objectives to learn stable representations for organic molecules. First, a joint embedding predictive architecture ensures that the representations capture structural information such that node-level embeddings are predictable. Second, causal representation learning ensures that the representations exhibit causal relationships with molecular properties. These causal mechanisms are governed by conditional probability distributions, leading to causal models that can evaluate and optimize your molecules.
Highlights
- Deploy the HGN encoder model to embed your organic molecules into representations of fixed dimensionality.
- Deploy the causal model to predict your molecule's properties and assess if the molecule is necessary and/or sufficient to cause a desired property using counterfactuals.
- Additionally, deploy the Neopoly Molecule algorithm to optimize your molecule based on predictions from the causal model.
Details
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Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.r5.8xlarge Training Recommended | Algorithm training on the ml.r5.8xlarge instance type | $0.00 |
ml.r7i.8xlarge Inference (Batch) Recommended | Model inference on the ml.r7i.8xlarge instance type, batch mode | $0.00 |
ml.r7i.8xlarge Inference (Real-Time) Recommended | Model inference on the ml.r7i.8xlarge instance type, real-time mode | $0.00 |
ml.p3.8xlarge Training | Algorithm training on the ml.p3.8xlarge instance type | $0.00 |
ml.m5.4xlarge Training | Algorithm training on the ml.m5.4xlarge instance type | $0.00 |
ml.p2.8xlarge Training | Algorithm training on the ml.p2.8xlarge instance type | $0.00 |
ml.p3.8xlarge Inference (Batch) | Model inference on the ml.p3.8xlarge instance type, batch mode | $0.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $0.00 |
ml.p2.8xlarge Inference (Batch) | Model inference on the ml.p2.8xlarge instance type, batch mode | $0.00 |
ml.p3.8xlarge Inference (Real-Time) | Model inference on the ml.p3.8xlarge instance type, real-time mode | $0.00 |
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Delivery details
Amazon SageMaker algorithm
An Amazon SageMaker algorithm is a machine learning model that requires your training data to make predictions. Use the included training algorithm to generate your unique model artifact. Then deploy the model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
Trained against the Harvard Organic Photovoltaic (HOPV) dataset Base architecture for hierarchical graph network: emb_dim = 32; gnn_layers = 6; gnn_type = gin; JK = concat
Additional details
Inputs
- Summary
Prepare "transform/" data directory for inferencing
data/ training/ transform/ transform_test.csv
- Input MIME type
- text/csv
https://github.com/neopolyai/molecule/blob/main/data/transform/transform_test.csv
https://github.com/neopolyai/molecule/blob/main/data/transform/transform_test.csv
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
Candidate | "Candidate" is the molecule SMILES you want to evaluate and optimize, while "Reference" is the molecule SMILES you want to compare the candidate molecule to. The reference molecule can be the state-of-the-art molecule for your use case an empty cell. "Complement" is the filler you used, which in this use case is the observed evidence. "Fill_factor" is the desired property of the organic photovoltaic. | Type: FreeText | Yes |
Reference | "Candidate" is the molecule SMILES you want to evaluate and optimize, while "Reference" is the molecule SMILES you want to compare the candidate molecule to. The reference molecule can be the state-of-the-art molecule for your use case an empty cell. "Complement" is the filler you used, which in this use case is the observed evidence. "Fill_factor" is the desired property of the organic photovoltaic. | Type: FreeText | Yes |
Complement | "Candidate" is the molecule SMILES you want to evaluate and optimize, while "Reference" is the molecule SMILES you want to compare the candidate molecule to. The reference molecule can be the state-of-the-art molecule for your use case an empty cell. "Complement" is the filler you used, which in this use case is the observed evidence. "Fill_factor" is the desired property of the organic photovoltaic. | Type: FreeText | Yes |
Fill_factor | "Candidate" is the molecule SMILES you want to evaluate and optimize, while "Reference" is the molecule SMILES you want to compare the candidate molecule to. The reference molecule can be the state-of-the-art molecule for your use case an empty cell. "Complement" is the filler you used, which in this use case is the observed evidence. "Fill_factor" is the desired property of the organic photovoltaic. | Type: FreeText | Yes |
Resources
Vendor resources
Support
Vendor support
Customize the Neopoly algorithm for your use case; reach out to us at hello@neopolyai.comÂ
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