Machine learning model distillation for protein design
AssigneeInternational Business Machines Corporation
InventorsIgor Melnyk, Aurelie Chloe Lozano, Payel Das, Enara C. Vijil
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AssigneeInternational Business Machines Corporation
InventorsIgor Melnyk, Aurelie Chloe Lozano, Payel Das, Enara C. Vijil
Estimating the term…
A distilled machine learning model is produced via initializing a first model with initial weights. An input protein sequence is input into both the first model and a folding protein model, wherein the inputting to the first model generates logits, and wherein the inputting to the folding protein model generates one or more predictive metrics. The one or more predictive metrics are discretized into classes and a first cross-entropy loss is computed based on the logits and the classes. The first model is optimized based on the first cross-entropy loss so that the optimized first model is the distilled machine learning model and an additional machine learning model is trained, using the distilled machine learning model, to perform a downstream protein modeling task.
A computer-implemented method comprising:
A computer program product, comprising:
A system comprising:
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