Systems and methods for spatial graph convolutions with applications to drug discovery and molecular simulation

A system comprising:

• a non-transitory memory communicatively coupled to the one or more computers, wherein the non-transitory memory stores instructions that, when executed by the one or more computers, cause the one or more computers to perform operations for training a graph neural network having a plurality of weights to perform molecular property prediction, the operations comprising:
• obtaining spatial distance data for a plurality of molecules, wherein the plurality of molecules comprises a plurality of atoms, wherein the spatial distance data defines, for each pair of atoms from the plurality of molecules, a respective three-dimensional (3D) spatial distance between the pair of atoms in a 3D spatial configuration of the atoms in the plurality of molecules;
• generating data defining a graph representing the plurality of molecules based at least in part on the spatial distance data defining 3D spatial distances between pairs of atoms from the plurality of molecules, wherein:
• the graph representing the plurality of molecules comprises a set of nodes and a set of edges, and each node in the graph represents a respective atom from the plurality of molecules;
• generating the data defining the graph comprises:
• determining, for each pair of nodes in the graph, whether the pair of nodes should be connected by an edge based at least in part on a 3D spatial distance between a corresponding pair of atoms in the 3D spatial configuration of the atoms in the plurality of molecules;
• and
• generating, for each pair of nodes in the graph that should be connected by an edge, a respective edge that connects the pair of nodes in the graph;
• and
• the set of edges of the graph comprises a plurality of inter-molecule edges, wherein each inter-molecule edge connects a respective pair of nodes representing a pair of atoms comprising a first atom in a first molecule and a second atom in a second, different molecule;
• performing a set of graph convolutions, by the a graph neural network and in accordance with a plurality of weights of the graph neural network, on the graph representing the plurality of molecules;
• processing, by the graph neural network and in accordance with the plurality of weights of the graph neural network, a result of the set of graph convolutions to generate predicting a set of one or more characteristics for the plurality of molecules;
• determining gradients of a loss function that measures an error in the prediction, generated by the graph neural network, of the set of one or more characteristics for the plurality of molecules;
• and
• updating the plurality of weights of the graph neural network using the gradients of the loss function.

A non-transitory memory storing instructions that when executed by one or more computers cause the one or more computers to perform operations for training a graph neural network having a plurality of weights to perform molecular property prediction, the operations comprising:

• obtaining spatial distance data for a plurality of molecules, wherein the plurality of molecules comprise a plurality of atoms, wherein the spatial distance data defines, for each pair of atoms from the plurality of molecules, a respective three-dimensional (3D) spatial distance between the pair of atoms in a 3D spatial configuration of the atoms in the plurality of molecules;
• generating data defining a graph representing the plurality of molecules based at least in part on the spatial distance data defining 3D spatial distances between pairs of atoms from the plurality of molecules, wherein:
• the graph representing the plurality of molecules comprises a set of nodes and a set of edges, and each node in the graph represents a respective atom from the plurality of molecules;
• generating the data defining the graph comprises:
• determining, for each pair of nodes in the graph, whether the pair of nodes should be connected by an edge based at least in part on a 3D spatial distance between a corresponding pair of atoms in the 3D spatial configuration of the atoms in the plurality of molecules;
• and
• generating, for each pair of nodes in the graph that should be connected by an edge, a respective edge that connects the pair of nodes in the graph;
• and
• the set of edges of the graph comprises a plurality of inter-molecule edges, wherein each inter-molecule edge connects a respective pair of nodes representing a pair of atoms comprising a first atom in a first molecule and a second atom in a second, different molecule;
• performing a set of graph convolutions, by a graph neural network and in accordance with a plurality of weights of the graph neural network, on the graph representing the plurality of molecules;
• processing, by the graph neural network and in accordance with the plurality of weights of the graph neural network, a result of the set of graph convolutions to generate a set of one or more characteristics for the plurality of molecules;
• determining gradients of a loss function that measures an error in the prediction, generated by the graph neural network, of the set of one or more characteristics for the plurality of molecules;
• and
• updating the plurality of weights of the graph neural network using the gradients of the loss function.