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 this https URL

我们介绍了一种基于好奇心驱动的方法，通过在化学空间的许多部分导航，从而实现更高的理想性和多样性。这种方法通过训练一个基于递归神经网络的分子生成器，并在此基础上进行微调，通过最大化好奇心和理想性来发现更多以及更易合成的理想分子。