ChemRxiv
These are preliminary reports that have not been peer-reviewed. They should not be regarded as conclusive, guide clinical practice/health-related behavior, or be reported in news media as established information. For more information, please see our FAQs.
Memory-assisted reinforcement learning for diverse molecular de novo design.pdf (1.5 MB)

Memory-Assisted Reinforcement Learning for Diverse Molecular De Novo Design

preprint
submitted on 22.07.2020 and posted on 23.07.2020 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.

Funding

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

History

Email Address of Submitting Author

thomas.blaschke@uni-bonn.de

Institution

University of Bonn

Country

Germany

ORCID For Submitting Author

0000-0003-2674-799X

Declaration of Conflict of Interest

no conflict of interest

Exports