Amazon Technologies, Inc.

Yen Ling Adelene Sim

Layne Christopher Price

Franziska Seeger

Alexander Sewall Ford

Andrew Borthwick

Vanessa Nguyen

Zachary Wu

Techniques for predicting a pair of an enzyme primary sequence and a substrate, and interaction probability for the pair are described. An exemplary method includes receiving a request to predict a pair of an enzyme primary sequence and a substrate, and interaction probability for the pair; combining an enzyme vector, a substrate vector, and an interaction indication for the enzyme and substrate to form a machine learning model input; applying a machine learning model to the machine learning model input to predict the pair of an enzyme primary sequence and a substrate, and interaction probability for the pair; and outputting a result of the application of the machine learning model including the predicted pair of an enzyme primary sequence and a substrate, and interaction probability for the pair.

Systems, methods, and apparatuses to predict pairs of enzyme primary sequences and substrates and an interaction probability for each predicted pair

Techniques for predicting a protein sequence are described. An exemplary method includes receiving a request to predict a missing area of a protein's primary sequence and a corresponding three-dimensional position of the missing area; applying a machine learning model to backbone Cartesian coordinates of the protein's primary sequence and a protein vector of a representation of the protein's primary sequence including the missing area to predict a missing area of the protein primary sequence and a corresponding three-dimensional position for the missing area, wherein the machine learning model is selected from the group consisting of: an attention-based machine learning model, a bidirectional long short term memory-based model, and a convolutional neural network-based model; and outputting a result of the machine learning model.

Systems, methods, and apparatuses to predict protein sequence and structure

Techniques for generating record embeddings from structured records are described. A record embeddings generating engine processes structured records to build a token vocabulary. Token embeddings are created for each token in the vocabulary. The token embeddings are trained using a loss function that relates the token embeddings to the record-attribute-data structure of the structured records. A record embedding is assembled from the trained token embeddings.

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Seattle, WA, United States of America

Yen Ling Adelene Sim