Memory-Assisted Reinforcement Learning for Diverse Molecular De Novo Design

23 July 2020, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


In de novo molecular design, recurrent neural networks (RNN) have been shown to be effective methods for sampling and generating novel chemical structures. Using a technique called reinforcement learning (RL), an RNN can be tuned to target a particular section of chemical space with optimized desirable properties using a scoring function. However, ligands generated by current RL methods so far tend to have relatively low diversity, and sometimes even result in duplicate structures when optimizing towards particular properties. Here, we propose a new method to address the low diversity issue in RL. Memory-assisted RL is an extension of the known RL, with the introduction of a so-called memory unit.


machine learning
Deep Learning Applications
Reinforcement Learning
de novo design
Recurrent Neural Networks


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