Deep Molecular Dreaming: Inverse machine learning for de-novo molecular design and interpretability with surjective representations

TL;DR

This paper introduces PASITHEA, a novel gradient-based method for de-novo molecular design using surjective SELFIES representations, enabling direct optimization and interpretability of chemical properties.

Contribution

PASITHEA is the first approach to apply inceptionism techniques to molecular design with SELFIES, allowing direct property optimization and model interpretability.

Findings

Preliminary results show a shift in property distribution during inverse training.

PASITHEA demonstrates the viability of gradient-based molecular optimization.

The method enables probing the model's understanding of chemical space.

Abstract

Computer-based de-novo design of functional molecules is one of the most prominent challenges in cheminformatics today. As a result, generative and evolutionary inverse designs from the field of artificial intelligence have emerged at a rapid pace, with aims to optimize molecules for a particular chemical property. These models 'indirectly' explore the chemical space; by learning latent spaces, policies, distributions or by applying mutations on populations of molecules. However, the recent development of the SELFIES string representation of molecules, a surjective alternative to SMILES, have made possible other potential techniques. Based on SELFIES, we therefore propose PASITHEA, a direct gradient-based molecule optimization that applies inceptionism techniques from computer vision. PASITHEA exploits the use of gradients by directly reversing the learning process of a neural network, which is trained to predict real-valued chemical properties. Effectively, this forms an inverse regression model, which is capable of generating molecular variants optimized for a certain property. Although our results are preliminary, we observe a shift in distribution of a chosen property during inverse-training, a clear indication of PASITHEA's viability. A striking property of inceptionism is that we can directly probe the model's understanding of the chemical space it was trained on. We expect that extending PASITHEA to larger datasets, molecules and more complex properties will lead to advances in the design of new functional molecules as well as the interpretation and explanation of machine learning models.