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Memory-assisted reinforcement learning for diverse molecular de novo design.pdf (1.5 MB)

Memory-Assisted Reinforcement Learning for Diverse Molecular De Novo Design

submitted on 22.07.2020, 23:41 and posted on 23.07.2020, 11:10 by Thomas Blaschke, Ola Engkvist, Jürgen Bajorath, Hongming Chen

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.


European Union’s Horizon 2020 research and innovation program; Marie Skłodowska-Curie grant agreement no. 676434, “Big Data in Chemistry” (“BIGCHEM,”


Email Address of Submitting Author


University of Bonn



ORCID For Submitting Author


Declaration of Conflict of Interest

no conflict of interest