Distributed Reinforcement Learning for Molecular Design: Antioxidant
  case
Huanyi Qin Denis Akhiyarov Sophie Loehle Kenneth Chiu Mauricio Araya-Polo
Deep reinforcement learning has successfully been applied for molecular
discovery as shown by the Molecule Deep Q-network (MolDQN) algorithm. This
algorithm has challenges when applied to optimizing new molecules: training
such a model is limited in terms of scalability to larger datasets and the
trained model cannot be generalized to different molecules in the same dataset.
In this paper, a distributed reinforcement learning algorithm for antioxidants,
called DA-MolDQN is proposed to address these problems. State-of-the-art bond
dissociation energy (BDE) and ionization potential (IP) predictors are
integrated into DA-MolDQN, which are critical chemical properties while
optimizing antioxidants. Training time is reduced by algorithmic improvements
for molecular modifications. The algorithm is distributed, scalable for up to
512 molecules, and generalizes the model to a diverse set of molecules. The
proposed models are trained with a proprietary antioxidant dataset. The results
have been reproduced with both proprietary and public datasets. The proposed
molecules have been validated with DFT simulations and a subset of them
confirmed in public "unseen" datasets. In summary, DA-MolDQN is up to 100x
faster than previous algorithms and can discover new optimized molecules from
proprietary and public antioxidants.