FREED++: Improving RL Agents for Fragment-Based Molecule Generation by
  Thorough Reproduction
Alexander Telepov Artem Tsypin Kuzma Khrabrov Sergey Yakukhnov Pavel Strashnov Petr Zhilyaev Egor Rumiantsev Daniel Ezhov Manvel Avetisian Olga Popova Artur Kadurin
A rational design of new therapeutic drugs aims to find a molecular structure
with desired biological functionality, e.g., an ability to activate or suppress
a specific protein via binding to it. Molecular docking is a common technique
for evaluating protein-molecule interactions. Recently, Reinforcement Learning
(RL) has emerged as a promising approach to generating molecules with the
docking score (DS) as a reward. In this work, we reproduce, scrutinize and
improve the recent RL model for molecule generation called FREED
(arXiv:2110.01219). Extensive evaluation of the proposed method reveals several
limitations and challenges despite the outstanding results reported for three
target proteins. Our contributions include fixing numerous implementation bugs
and simplifying the model while increasing its quality, significantly extending
experiments, and conducting an accurate comparison with current
state-of-the-art methods for protein-conditioned molecule generation. We show
that the resulting fixed model is capable of producing molecules with superior
docking scores compared to alternative approaches.