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Deep learning algorithms applied to computational chemistry

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Abstract

Recently, there has been a significant increase in the use of deep learning techniques in the molecular sciences, which have shown high performance on datasets and the ability to generalize across data. However, no model has achieved perfect performance in solving all problems, and the pros and cons of each approach remain unclear to those new to the field. Therefore, this paper aims to review deep learning algorithms that have been applied to solve molecular challenges in computational chemistry. We proposed a comprehensive categorization that encompasses two primary approaches; conventional deep learning and geometric deep learning models. This classification takes into account the distinct techniques employed by the algorithms within each approach. We present an up-to-date analysis of these algorithms, emphasizing their key features and open issues. This includes details of input descriptors, datasets used, open-source code availability, task solutions, and actual research applications, focusing on general applications rather than specific ones such as drug discovery. Furthermore, our report discusses trends and future directions in molecular algorithm design, including the input descriptors used for each deep learning model, GPU usage, training and forward processing time, model parameters, the most commonly used datasets, libraries, and optimization schemes. This information aids in identifying the most suitable algorithms for a given task. It also serves as a reference for the datasets and input data frequently used for each algorithm technique. In addition, it provides insights into the benefits and open issues of each technique, and supports the development of novel computational chemistry systems.

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Notes

  1. For readers who are not yet familiar with deep learning concepts, it is highly recommended to explore the following references [19,20,21].

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Acknowledgements

The present study was carried out under the grant Ciencia de Frontera 2019 from CONAHCYT, CF-2019\1311317, at the Faculty of Medicine and Biomedical Sciences of the Universidad Autónoma de Chihuahua, México.

Funding

This work was funded by Consejo Nacional de Ciencia y Tecnología, CONACYT CdF-2019/1311317, CONACYT CdF-2019/1311317, CONACYT CdF-2019/1311317, CONACYT CdF-2019/1311317.

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Authors and Affiliations

  1. Computational Chemistry Physics Laboratory, Facultad de Medicina y Ciencias Biomédicas, Universidad Autónoma de Chihuahua, Campus II, 31125, Chihuahua, Mexico

    Abimael Guzman-Pando, Carlos Arzate-Quintana & Javier Camarillo-Cisneros

  2. Faculty of Engineering, Universidad Autónoma de Chihuahua, Campus II, 31125, Chihuahua, Mexico

    Graciela Ramirez-Alonso

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  1. Abimael Guzman-Pando
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A.G.P. wrote the main manuscript text and prepared figures, J.C.C wrote the main manuscript text prepared tables, All authors reviewed the manuscript.

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Correspondence to Javier Camarillo-Cisneros.

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Guzman-Pando, A., Ramirez-Alonso, G., Arzate-Quintana, C. et al. Deep learning algorithms applied to computational chemistry. Mol Divers (2023). https://doi.org/10.1007/s11030-023-10771-y

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