International Business Machines Corporation

Igor Melnyk

Pierre L Dognin

Payel Das

Youssef Mroueh

Enara C Vijil

Pin-Yu Chen

Cicero Nogueira Dos Santos

Inkit Padhi

Tom Sercu

Tom D J Sercu

Jarret Ross

Yue Cao

A system and method for designing protein sequences conditioned on a specific target fold. The system is a transformer-based generative framework for modeling a complex sequence-structure relationship. To mitigate the heterogeneity between the sequence domain and the fold domain, a Fold-to-Sequence model jointly learns a sequence embedding using a transformer and a fold embedding from the density of secondary structural elements in 3D voxels. The joint sequence-fold representation through novel intra-domain and cross-domain losses with an intra-domain loss forces two semantically similar (where the proteins should have the same fold(s)) samples from the same domain to be close to each other in a latent space, while a cross-domain loss forces two semantically similar samples in different domains to be closer. In an embodiment, the Fold-to-Sequence model performs design tasks that include low resolution structures, structures with a region of missing residues, and NMR structural ensembles.

Embedding-based generative model for protein design

A method, system and apparatus of ensembling, including inputting a set of models that predict different sets of attributes, determining a source set of attributes and a target set of attributes using a barycenter with an optimal transport metric, and determining a consensus among the set of models whose predictions are defined on the source set of attributes.

Wasserstein barycenter model ensembling

Systems, computer-implemented methods, and computer program products to facilitate a dual learning bridge between text and a knowledge graph are provided. According to an embodiment, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components comprise a model component that employs a model to learn associations between text data and a knowledge graph. The computer executable components further comprise a translation component that uses the model to bidirectionally translate second text data and one or more knowledge graph paths based on the associations.

Dual learning bridge between text and knowledge graph

A computing device receives a data X and Y, each having N samples. A function f(x,y) is defined to be a trainable neural network based on the data X and the data Y. A permuted version of the data Y is created. A loss mean is computed based on the trainable neural network f(x,y), the permuted version of the sample data Y, and a trainable scalar variable &#x3b7;. A loss with respect to the scalar variable &#x3b7; and the trainable neural network is minimized. Upon determining that the loss is at or below the predetermined threshold, estimating a mutual information (MI) between a test data Xand Y. If the estimated MI is above a predetermined threshold, the test data Xand Yis deemed to be dependent. Otherwise, it is deemed to be independent.

Mutual information neural estimation with Eta-trick

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White Plains, NY, United States of America

Igor Melnyk