Deep learning-based molecular dynamics simulation for structure-based drug design against SARS-CoV-2

Sun, Yao; Jiao, Yanqi; Shi, Chengcheng; Zhang, Yang

doi:10.1016/j.csbj.2022.09.002

Cited by 11 publications

(8 citation statements)

References 134 publications

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“…Researchers can now understand the binding mode, affinity, and evolution of atomic systems by using appropriate models and algorithms that allow the chosen model to “learn” the patterns present in the input data. This is especially true of advances made in the use of DL-based MD computational methods [140] .…”

Section: Structure-based Drug Designmentioning

confidence: 99%

AI in drug discovery and its clinical relevance

Qureshi

Irfan

Gondal

et al. 2023

Heliyon

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Section: Structure-based Drug Designmentioning

confidence: 99%

AI in drug discovery and its clinical relevance

Qureshi

Irfan

Gondal

et al. 2023

Heliyon

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“…The developed model has shown reasonable predictions for various ground-state QM properties. Similarly integrating AiKPro with such in-silico techniques may offer a comprehensive understanding of kinase-ligand interaction, elucidation their crucial role in drug discovery and specificity 62 , 63 . This synergistic approach can provide a more accurate and holistic view of kinase inhibitor interactions, guiding the design of targeted and effective therapeutics.…”

Section: Discussionmentioning

confidence: 99%

AiKPro: deep learning model for kinome-wide bioactivity profiling using structure-based sequence alignments and molecular 3D conformer ensemble descriptors

Park¹,

Hong²,

Lee³

et al. 2023

Sci Rep

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The discovery of selective and potent kinase inhibitors is crucial for the treatment of various diseases, but the process is challenging due to the high structural similarity among kinases. Efficient kinome-wide bioactivity profiling is essential for understanding kinase function and identifying selective inhibitors. In this study, we propose AiKPro, a deep learning model that combines structure-validated multiple sequence alignments and molecular 3D conformer ensemble descriptors to predict kinase-ligand binding affinities. Our deep learning model uses an attention-based mechanism to capture complex patterns in the interactions between the kinase and the ligand. To assess the performance of AiKPro, we evaluated the impact of descriptors, the predictability for untrained kinases and compounds, and kinase activity profiling based on odd ratios. Our model, AiKPro, shows good Pearson’s correlation coefficients of 0.88 and 0.87 for the test set and for the untrained sets of compounds, respectively, which also shows the robustness of the model. AiKPro shows good kinase-activity profiles across the kinome, potentially facilitating the discovery of novel interactions and selective inhibitors. Our approach holds potential implications for the discovery of novel, selective kinase inhibitors and guiding rational drug design.

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“…With the development of structural biology, molecular biology, and chemical informatics, SBVS has become the mainstream of virtual screening. Its related technologies mainly include molecular docking, receptor-based pharmacophore [ 28 ], molecular dynamics simulation [ 29 ], etc. In contrast, LBVS generally does not consider the protein structure and instead focuses on analyzing the properties of ligands.…”

Section: Methodsmentioning

confidence: 99%

Improving structure-based protein-ligand affinity prediction by graph representation learning and ensemble learning

Guo

2024

PLoS ONE

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Predicting protein-ligand binding affinity presents a viable solution for accelerating the discovery of new lead compounds. The recent widespread application of machine learning approaches, especially graph neural networks, has brought new advancements in this field. However, some existing structure-based methods treat protein macromolecules and ligand small molecules in the same way and ignore the data heterogeneity, potentially leading to incomplete exploration of the biochemical information of ligands. In this work, we propose LGN, a graph neural network-based fusion model with extra ligand feature extraction to effectively capture local features and global features within the protein-ligand complex, and make use of interaction fingerprints. By combining the ligand-based features and interaction fingerprints, LGN achieves Pearson correlation coefficients of up to 0.842 on the PDBbind 2016 core set, compared to 0.807 when using the features of complex graphs alone. Finally, we verify the rationalization and generalization of our model through comprehensive experiments. We also compare our model with state-of-the-art baseline methods, which validates the superiority of our model. To reduce the impact of data similarity, we increase the robustness of the model by incorporating ensemble learning.

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Deep learning-based molecular dynamics simulation for structure-based drug design against SARS-CoV-2

Cited by 11 publications

References 134 publications

AI in drug discovery and its clinical relevance

AI in drug discovery and its clinical relevance

AiKPro: deep learning model for kinome-wide bioactivity profiling using structure-based sequence alignments and molecular 3D conformer ensemble descriptors

Improving structure-based protein-ligand affinity prediction by graph representation learning and ensemble learning

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