A systematic review of deep learning chemical language models in recent era

A systematic review of deep learning chemical language models in recent era

Abstract Discovering new chemical compounds with specific properties can provide advantages for fields that rely on materials for their development, although this task comes at a high cost in terms of complexity and resources. Since the beginning of the data age, deep learning techniques have revolu...

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Bibliographic Details
Main Authors: Hector Flores-Hernandez, Emmanuel Martinez-Ledesma
Format: Article
Language:English
Published: BMC 2024-11-01
Series:Journal of Cheminformatics
Subjects:
Chemical language models (CLMs)
Recurrent neural networks (RNNs)
Transformers
Variational autoencoders (VAEs)
Generative adversarial networks (GANs)
Transfer learning
Online Access:https://doi.org/10.1186/s13321-024-00916-y

Internet

https://doi.org/10.1186/s13321-024-00916-y

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