System and method for de novo drug discovery

A system for de novo drug discovery, comprising:

• a computer system comprising a memory and a processor;
• a de novo discovery module, comprising a first plurality of programming instructions stored in the memory and operating on the processor, wherein the first plurality of programming instructions, when operating on the processor, causes the computer system to:
• receive a voxel-based representation of one or more molecules;
• use the voxel-based representation of the one or more molecules to train a variational autoencoder, wherein the variational autoencoder determines where each molecule lies in a latent space;
• and
• use the decoder portion of the variational autoencoder as a vector input to a bioactivity model, wherein the vector input comprises one or more small molecule vector representations;
• and
• a bioactivity model comprising a second plurality of programming instructions stored in the memory and operating on the processor, wherein the second plurality of programming instructions, when operating on the processor, cause the computer system to:
• train a model of voxel-based representations on a range of one or more large molecules that comprise desired molecular properties;
• generate a concatenated vector comprising the one or more small molecule vector inputs and a vector representation of the one or more large molecules;
• output the concatenated vector to the latent space, wherein the concatenated vector output compresses the latent space;
• sample the compressed latent space, wherein the compressed latent space comprises a candidate set of latent examples;
• and
• reconstruct the candidate set of latent examples to arrive at a candidate set of molecules that match the desired molecular properties.

A method for de novo drug discovery, comprising the steps of:

• receiving molecule training data comprising one or more representations of one or more chemical formulas;
• enriching the molecule training data by querying data sources to find similar molecules or molecules with similar bioactivity;
• using the enriched molecule training data to train an encoder of a neural network, wherein the encoder determines where each molecule in the enriched molecule training data lies in a latent space;
• determining a subspace of the latent space comprising a candidate set of latent examples;
• sampling points within the subspace and perform interpolations, perturbations, or both on the sample points to expand the candidate set of latent examples;
• and
• decoding the candidate set of latent examples to reconstruct a candidate set of chemically valid molecules.