Drug compound identification for target tissue cells

Abstract

Provide a reinforcement learning model including an agent and a critic; the critic includes a neural network pre-trained to generate, from input biomolecular data characterizing tissue cells and input compound data defining a compound molecule, a property value for said biomolecular action of that molecule on those tissue cells. The agent includes a neural network adapted to generate the compound data in dependence on input biomolecular data. Supply biomolecular data characterizing patient tissue cells to the agent and supply that data, and the compound data generated therefrom, to the critic to obtain a property value in an iterative training process in which reward values, dependent on the property values, are used to progressively train the agent to optimize the reward value. After training the agent, supply target biomolecular data, characterizing the target tissue cells, to the agent to generate compound data corresponding to a set of drug compounds.