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Erikawa_2021.pdf (2.88 MB)

MERMAID: An Open Source Automated Hit-to-Lead Method Based on Deep Reinforcement Learning

submitted on 20.04.2021, 00:30 and posted on 20.04.2021, 10:41 by Daiki Erikawa, Nobuaki Yasuo, Masakazu Sekijima
The hit-to-lead process makes the physicochemical properties of the hit compounds that show the desired type of activity obtained in the screening assay more drug-like. Deep learning-based molecular generative models are expected to contribute to the hit-to-lead process.
The simplified molecular input line entry system (SMILES), which is a string of alphanumeric characters representing the chemical structure of a molecule, is one of the most commonly used representations of molecules, and molecular generative models based on SMILES have achieved significant success. However, in contrast to molecular graphs, during the process of generation, SMILES are not considered as valid SMILES. Further, it is quite difficult to generate molecules starting from a certain molecule, thus making it difficult to apply SMILES to the hit-to-lead process.In this study, we have developed a SMILES-based generative model that can be generated starting from a certain compound. This method generates partial SMILES and inserts it into the original SMILES using Monte Carlo Tree Search and a Recurrent Neural Network.We validated our method using a molecule dataset obtained from the ZINC database and successfully generated molecules that were both well optimized for the objectives of the quantitative estimate of drug-likeness (QED) and penalized octanol-water partition coefficient (PLogP) optimization.
The source code is available at https: //


Platform Project for Supporting Drug Discovery and Life Science Research (Basis for Supporting Innovative Drug Discovery and Life Science Research (BINDS)) from AMED under Grant Number JP20am0101112

Japan Society for the Promotion of Science (JSPS) KAKENHI Grant Numbers 20H00620


Email Address of Submitting Author


Tokyo Institute of Technology



ORCID For Submitting Author


Declaration of Conflict of Interest

The authors declare no competing interests.