Abstract
Recent methods for generating novel molecules use graph representations of
molecules and employ various forms of graph convolutional neural networks for
inference. However, training requires solving an expensive graph isomorphism
problem, which previous approaches do not address or solve only approximately.
In this work, we propose ALMGIG, a likelihood-free adversarial learning
framework for inference and de novo molecule generation that avoids explicitly
computing a reconstruction loss. Our approach extends generative adversarial
networks by including an adversarial cycle-consistency loss to implicitly
enforce the reconstruction property. To capture properties unique to
molecules, such as valence, we extend the Graph Isomorphism Network to
multi-graphs. To quantify the performance of models, we propose to compute
the distance between distributions of physicochemical properties with the
1-Wasserstein distance. We demonstrate that ALMGIG more accurately learns the
distribution over the space of molecules than all baselines. Moreover, it can
be utilized for drug discovery by efficiently searching the space of molecules
using molecules’ continuous latent representation. Our code is available
at
https://github.com/ai-med/almgigPublication
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD)