  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.
