  One challenging and essential task in biochemistry is the generation of novel
molecules with desired properties. Novel molecule generation remains a
challenge since the molecule space is difficult to navigate through, and the
generated molecules should obey the rules of chemical valency. Through this
work, we propose a novel method, which we call unitMCTS, to perform molecule
generation by making a unit change to the molecule at every step using Monte
Carlo Tree Search. We show that this method outperforms the recently published
techniques on benchmark molecular optimization tasks such as QED and penalized
logP. We also demonstrate the usefulness of this method in improving molecule
properties while being similar to the starting molecule. Given that there is no
learning involved, our method finds desired molecules within a shorter amount
of time.
