DEFactor: Differentiable Edge Factorization-based Probabilistic Graph Generation
This work addresses a crucial step in drug discovery and other industries by enabling efficient and differentiable conditional graph generation for molecules, though it appears incremental as it builds on existing deep generative models for graph generation.
The authors tackled the problem of generating novel molecules with optimal properties by proposing a differentiable graph generation model that avoids graph size dependency and discrete action sequences, enabling conditional graph generation and direct optimization of graphs, and demonstrated favorable performance on prototype-based molecular graph conditional generation tasks.
Generating novel molecules with optimal properties is a crucial step in many industries such as drug discovery. Recently, deep generative models have shown a promising way of performing de-novo molecular design. Although graph generative models are currently available they either have a graph size dependency in their number of parameters, limiting their use to only very small graphs or are formulated as a sequence of discrete actions needed to construct a graph, making the output graph non-differentiable w.r.t the model parameters, therefore preventing them to be used in scenarios such as conditional graph generation. In this work we propose a model for conditional graph generation that is computationally efficient and enables direct optimisation of the graph. We demonstrate favourable performance of our model on prototype-based molecular graph conditional generation tasks.