Realizing private and practical pharmacological collaboration using a neural network architecture configured for reduced computation overhead

Abstract

Computationally-efficient techniques facilitate secure pharmacological collaboration with respect to private drug target interaction (DTI) data. In one embodiment, a method begins by receiving, via a secret sharing protocol, observed DTI data from individual participating entities. A secure computation then is executed against the secretly-shared data to generate a pooled DTI dataset. For increased computational efficiency, at least a part of the computation is executed over dimensionality-reduced data. The resulting pooled DTI dataset is then used to train a neural network model. The model is then used to provide one or more DTI predictions that are then returned to the participating entities (or other interested parties).

Having described our invention, what we claim also is set forth below: 
     
         1 . A method for pharmacologic collaboration involving drug target interaction (DTI) data, the DTI data having been secretly shared to generate a pooled DTI dataset, wherein the secret sharing preserves privacy of individual drugs, targets and interactions, comprising:
 providing a neural network configured for reduced computation overhead by using an activation function, together with a hinge loss function that evaluates training scores, wherein at least one of the activation and hinge loss functions use a single data-oblivious comparison, thereby reducing the computation overhead;   training the neural network at least in part using gradient descent and with a random subset of training examples for at least one neural network parameter update iteration;   following training that occurs over a scalable runtime due at least in part to the reduced computation overhead, generating one or more DTI predictions for a drug discovery workflow using the neural network; and   outputting the one or more DTI predictions.   
     
     
         2 . The method as described in  claim 1  wherein the DTI data is associated with two or more collaborating entities. 
     
     
         3 . The method as described in  claim 1  wherein the DTI data comprises drug and target side information, the drug and target side information including an interaction score, and one or more feature vectors. 
     
     
         4 . The method as described in  claim 3  wherein the DTI data further include chemical structures and protein sequences describing a drug. 
     
     
         5 . The method as described in  claim 1  wherein the gradient descent is a stochastic gradient descent with Nesterov momentum. 
     
     
         6 . The method as described in  claim 1  wherein the training scales linearly with respect to a subset of the DTI data. 
     
     
         7 . The method as described in  claim 1  wherein the DTI data comprises greater than 10 6  drug target interactions and the training is executed over a time period measured in hours. 
     
     
         8 . The method as described in  claim 6  wherein the training is further executed over a wide area network (WAN). 
     
     
         9 . The method as described in  claim 1  wherein the activation function is a rectified linear unit (RELU) activation function.