Curiosity as a Self-Supervised Method to Improve Exploration in De novo
  Drug Design
Mohamed-Amine Chadi Hajar Mousannif Ahmed Aamouche
In recent years, deep learning has demonstrated promising results in de novo
drug design. However, the proposed techniques still lack an efficient
exploration of the large chemical space. Most of these methods explore a small
fragment of the chemical space of known drugs, if the desired molecules were
not found, the process ends. In this work, we introduce a curiosity-driven
method to force the model to navigate many parts of the chemical space,
therefore, achieving higher desirability and diversity as well. At first, we
train a recurrent neural network-based general molecular generator (G), then we
fine-tune G to maximize curiosity and desirability. We define curiosity as the
Tanimoto similarity between two generated molecules, a first molecule generated
by G, and a second one generated by a copy of G (Gcopy). We only backpropagate
the loss through G while keeping Gcopy unchanged. We benchmarked our approach
against two desirable chemical properties related to drug-likeness and showed
that the discovered chemical space can be significantly expanded, thus,
discovering a higher number of desirable molecules with more diversity and
potentially easier to synthesize. All Code and data used in this paper are
available at https://github.com/amine179/Curiosity-RL-for-Drug-Design.