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Generation of focused drug molecule library using recurrent neural network

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Abstract

Context

With the wide application of deep learning in drug research and development, de novo molecular design methods based on recurrent neural network (RNN) have strong advantages in drug molecule generation. The RNN model can be used to learn the internal chemical structure of molecules, which is similar to a natural language processing task. Although techniques for generating target-specific molecular libraries based on RNN models are mature, research related to drug design and screening continues around the clock. Research based on de novo drug design methods to generate larger quantities of valid compounds is necessary.

Methods

In this study, a molecular generation model based on RNN was designed, which abandoned the traditional way of stacked RNN and introduced the Nested long short-term memory network structure. To enrich the library of focused molecules for specific targets, we fine-tuned the model using active molecules from novel coronavirus pneumonia and screened the molecules using machine learning models. Following rigorous screening, the selected molecules underwent molecular docking with the SARS-CoV-2 M-pro receptor using AutoDock2.4 to identify the top 3 potential inhibitors. Subsequently, 100-ns molecular dynamics simulations were conducted using Amber22. Molecule parameterization involved the GAFF2 force field, while the proteins were modeled using the ff19SB force field, with solvation facilitated by a truncated octahedral TIP3P solvent environment. Upon completion of molecular dynamics simulations, stability of ligand-protein complexes was assessed by analysis of RMSD, H-bonds, and MM-GBSA. Reasonable results prove that the model can complete the task of de novo drug design and has the potential to be ideal drug molecules.

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Data availability

In this article, all data are available for download, for example, the pre-training dataset is available for download on https://www.ebi.ac.uk/chembl/ , and the N3 and 7BQY dataset are available for download on https://www.rcsb.org/ . All calculations in this study were performed in the TensorFlow-GPU 2.3.0 framework with Keras 2.8.0 and Python 3.7.13, which is freely available from: https://pypi.org/project/tensorflow/2.3.0/ and https://pypi.org/project/keras/2.8.0/ .

Code availability

The source code and training data used in the experiment are provided at: https://github.com/****1025/Drug_RNN .

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Funding

This work was supported by the National Natural Science Foundation of China (12261060) and the Jiangxi province graduate student innovation special funds (YC2021-S029).

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Authors and Affiliations

  1. Department of Mathematics, School of Mathematics and Computer Sciences, Nanchang University, Nanchang, 330031, China

    **** Zou, Long Zhao & Shao** Shi

  2. Institute of Mathematics and Interdisciplinary Sciences, Nanchang University, Nanchang, 330031, China

    **** Zou, Long Zhao & Shao** Shi

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  1. **** Zou
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  2. Long Zhao
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Contributions

All authors contributed to the study conception, design, and analysis. Model design and construction, material preparation, and data collection were performed by **** Zou. Long Zhao completed molecular docking and molecular dynamics simulation. The first draft of the manuscript was written by **** Zou. Shao** Shi commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Shao** Shi.

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Zou, J., Zhao, L. & Shi, S. Generation of focused drug molecule library using recurrent neural network. J Mol Model 29, 361 (2023). https://doi.org/10.1007/s00894-023-05772-5

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