Machine-learning foundation model for generating biopolymer embeddings
AssigneeAtomic AI, Inc.
InventorsNicholas Ranieri BOYD, Stephan Johannes EISMANN, Raphael John LAMARRE TOWNSHEND, Brent TOWNSHEND, Brandon Micheal ANDERSON
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Abstract
A property of interest for a biopolymer is predicted using a combined model that includes a foundation model and a task-specific model. The foundation model is trained at least in part using chemical mapping data and generates embeddings from biopolymer sequences. The task-specific model takes an embedding generated by the foundation model for a sequence and generates a prediction of whether the corresponding biopolymer molecule has a target property.
Claims18
A computer-implemented method of predicting a target property of a biomolecule, the method comprising:
- obtaining first training data, the first training data including first biopolymer sequences and corresponding experimentally obtained data;
- training a foundation model, using the first training data, to predict the experimentally obtained data from the biopolymer sequences;
- adding a task-specific model to the foundation model to create a combined model;
- training the combined model, using second training data, to predict the target property of biomolecules corresponding to second biopolymer sequences;
- and
- applying the combined model to a previously unseen biopolymer sequence to generate a prediction of whether a candidate biomolecule corresponding to the previously unseen biopolymer sequence has the target property.
A computer-implemented method of predicting a target property of a biomolecule, the method comprising:
- receiving a biopolymer sequence and an indication of the target property, the biopolymer sequence describing a biomolecule;
- selecting a combined model to apply, wherein the combined model was trained by a process comprising:
- obtaining first training data, the first training data including first biopolymer sequences and corresponding experimentally obtained data;
- training a foundation model, using the first training data, to predict the experimentally obtained data from the biopolymer sequences;
- adding a task-specific model to the foundation model to create the combined model;
- and
- training the combined model, using second training data, to predict the target property of biomolecules corresponding to second biopolymer sequences;
- applying the combined model to the biopolymer sequence to generate a prediction of whether the biomolecule has the target property;
- and
- providing the prediction for display.
A non-transitory computer-readable storage medium comprising computer program code that, when executed by a computing system, causes the computing system to perform operations including:
- receiving a biopolymer sequence and an indication of the target property, the biopolymer sequence describing a biomolecule;
- selecting a combined model to apply, wherein the combined model was trained by a process comprising:
- obtaining first training data, the first training data including first biopolymer sequences and corresponding experimentally obtained data;
- training a foundation model, using the first training data, to predict the experimentally obtained data from the biopolymer sequences;
- adding a task-specific model to the foundation model to create the combined model;
- and
- training the combined model, using second training data, to predict the target property of biomolecules corresponding to second biopolymer sequences;
- applying the combined model to the biopolymer sequence to generate a prediction of whether the biomolecule has the target property;
- and
- providing the prediction for display.
Dependencies are inferred from the claim text.