Drug efficacy prediction for treatment of genetic disease

Abstract

A neural network model is generated for drug efficacy prediction in treatment of genetic disease from a dataset correlating biomolecular data for disease-cell samples with drug efficacy values for a plurality of drugs, including defining training data pairs each including biomolecular data for a sample represented in the dataset and a string representation of a drug represented in the dataset; and using the training data pairs and the drug efficacy values corresponding to respective pairs to train weights of the model including a first attention-based encoder which encodes the biomolecular data of each pair, to produce encoded biomolecular data, a second attention-based encoder which encodes the string representation of each pair, to produce encoded string data, and a set of neural network layers which process the encoded biomolecular and string data for each pair, to produce a model output corresponding to drug efficacy.