Graph neural networks (GNNs) have become the standard toolkit for analyzing and learning from data on graphs. As the field grows, it becomes critical to identify key architectures and validate new ideas that generalize to larger, more complex …
We propose a simple auto-encoder framework for molecule generation. The molecular graph is first encoded into a continuous latent representation , which is then decoded back to a molecule. The encoding process is easy, but the decoding process …
In this talk, I will discuss a graph convolutional neural network architecture for the molecule generation task. The proposed approach consists of two steps. First, a graph ConvNet is used to auto-encode molecules in one-shot. Second, beam search is …
Chemical synthesis, structure and property prediction using deep neural networks.
In this talk, I will discuss how to apply graph convolutional neural networks to quantum chemistry and operational research. The same high-level paradigm can be applied to generate new molecules with optimized chemical properties and to solve the …