Abstract: The present disclosure relates to systems, methods, and products for identifying candidate molecule. The system includes a non-transitory memory storing instructions; and a processor in communication with the non-transitory memory. The processor executes the instructions to cause the system to receive drug data; convert the drug data into at least one point in a latent space using a grammar variational auto-encoder (VAE) model; receive a query for the at least one candidate molecule; select one or more points in the latent space; and create a k-dimensional tree graph based on the query for the at least one candidate molecule and the selected one or more points; determine a plurality of paths according to an interpolation technique; receive preference data; determine an optimum path; determine at least one candidate point on the optimum path; and determine a drug molecular structure using an inverse of the grammar VAE model.

Abstract: The present disclosure relates to systems, methods, and products for identifying candidate molecule. The system includes a non-transitory memory storing instructions; and a processor in communication with the non-transitory memory. The processor executes the instructions to cause the system to receive drug data; convert the drug data into at least one point in a latent space using a grammar variational auto-encoder (VAE) model; receive a query for the at least one candidate molecule; select one or more points in the latent space; and create a k-dimensional tree graph based on the query for the at least one candidate molecule and the selected one or more points; determine a plurality of paths according to an interpolation technique; receive preference data; determine an optimum path; determine at least one candidate point on the optimum path; and determine a drug molecular structure using an inverse of the grammar VAE model.