Transformer-based graph neural network trained with three-dimensional distance data

Abstract

A computing system is provided, including a processor configured to, during a training phase, provide a training data set including a pre-transformation molecular graph and post-transformation energy parameter value representing an energy change in a molecular system following an energy transformation. The pre-transformation molecular graph includes a plurality of normal nodes fully connected by edges. The processor is configured to encode structural information including a three-dimensional Euclidean distance along an edge connecting a pair of the normal nodes in each molecular graph as learnable embeddings. The processor is configured to input the training data set to a transformer-based graph neural network to train the network to perform an inference at inference time. The processor is further configured to receive inference-time input of the inference-time pre-transformation molecular graph at the trained transformer-based graph neural network, and output the inference-time post-transformation energy parameter value based on the inference-time pre-transformation molecular graph.