ArticleJournal of cheminformatics2021
MERMAID: an open source automated hit-to-lead method based on deep reinforcement learning.
Article in Journal of cheminformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Small data, big challenges: Machine- and deep-learning strategies for data-limited drug discovery.Advanced drug delivery reviews · 2026Review
- Reinforcement learning-based generative artificial intelligence for novel pesticide design.Journal of advanced research · 2025Article
- Generative Deep Learning for de Novo Drug Design─A Chemical Space Odyssey.Journal of chemical information and modeling · 2025Review
- Natural Language Processing Methods for the Study of Protein-Ligand Interactions.Journal of chemical information and modeling · 2025Review
- Molecular optimization using a conditional transformer for reaction-aware compound exploration with reinforcement learning.Communications chemistry · 2025Article
- DiffInt: A Diffusion Model for Structure-Based Drug Design with Explicit Hydrogen Bond Interaction Guidance.Journal of chemical information and modeling · 2025Article
- Molecular similarity: Theory, applications, and perspectives.Artificial intelligence chemistry · 2024Article
- Mothra: MultiobjectiveJournal of chemical information and modeling · 2024Article
- IEV2Mol: Molecular Generative Model Considering Protein-Ligand Interaction Energy Vectors.Journal of chemical information and modeling · 2024Article
- FOCUS on NOD2: Advancing IBD Drug Discovery with a User-Informed Machine Learning Framework.ACS medicinal chemistry letters · 2024Article
- ASD2023: towards the integrating landscapes of allosteric knowledgebase.Nucleic acids research · 2024Article
- Gargoyles: An Open Source Graph-Based Molecular Optimization Method Based on Deep Reinforcement Learning.ACS omega · 2023Article
- Discovery of a HiddenACS omega · 2023Article
- Computer-aided multi-objective optimization in small molecule discovery.Patterns (New York, N.Y.) · 2023Review
- Augmented Hill-Climb increases reinforcement learning efficiency for language-based de novo molecule generation.Journal of cheminformatics · 2022Article
- PCW-A1001, AI-assistedFrontiers in molecular biosciences · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
The hit-to-lead process makes the physicochemical properties of the hit molecules 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 molecule. 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://github.com/sekijima-lab/mermaid .
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.