Pharmacophore Modelling-Based Drug Repurposing Approaches for SARS-CoV-2 Therapeutics

Rampogu, Shailima; Lee, Keun Woo

doi:10.3389/fchem.2021.636362

Cited by 23 publications

(4 citation statements)

References 39 publications

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“…Among the five drugs KEDD identified, captopril and lisinopril are experimentally validated active compounds, whose binding affinity values are reported on PubChem [ 55 ]. Additionally, recent studies from the biomedical domain point out that vitamin C and enalaprilat also exhibit lowering effects on the protein [ 56 – 58 ], and an in silico work suggests that framycetin could be a potential ACE2 inhibitor [ 59 ].…”

Section: Resultsmentioning

confidence: 99%

Toward Unified AI Drug Discovery with Multimodal Knowledge

Luo,

Liu,

Yang

et al. 2024

Health Data Sci

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Background: In real-world drug discovery, human experts typically grasp molecular knowledge of drugs and proteins from multimodal sources including molecular structures, structured knowledge from knowledge bases, and unstructured knowledge from biomedical literature. Existing multimodal approaches in AI drug discovery integrate either structured or unstructured knowledge independently, which compromises the holistic understanding of biomolecules. Besides, they fail to address the missing modality problem, where multimodal information is missing for novel drugs and proteins. Methods: In this work, we present KEDD, a unified, end-to-end deep learning framework that jointly incorporates both structured and unstructured knowledge for vast AI drug discovery tasks. The framework first incorporates independent representation learning models to extract the underlying characteristics from each modality. Then, it applies a feature fusion technique to calculate the prediction results. To mitigate the missing modality problem, we leverage sparse attention and a modality masking technique to reconstruct the missing features based on top relevant molecules. Results: Benefiting from structured and unstructured knowledge, our framework achieves a deeper understanding of biomolecules. KEDD outperforms state-of-the-art models by an average of 5.2% on drug–target interaction prediction, 2.6% on drug property prediction, 1.2% on drug–drug interaction prediction, and 4.1% on protein–protein interaction prediction. Through qualitative analysis, we reveal KEDD’s promising potential in assisting real-world applications. Conclusions: By incorporating biomolecular expertise from multimodal knowledge, KEDD bears promise in accelerating drug discovery.

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Section: Resultsmentioning

confidence: 99%

Toward Unified AI Drug Discovery with Multimodal Knowledge

Luo,

Liu,

Yang

et al. 2024

Health Data Sci

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“…RMSD records the deviations present between the initial structure and the final structure of the simulation run. Generally, a smaller deviation denotes a relatively stable structure [ 43 , 44 ]. In the current study, the RMSD for the six systems has projected to be stable below 0.3 nm.…”

Section: Resultsmentioning

confidence: 99%

Explicit molecular dynamics simulation studies to discover novel natural compound analogues as Mycobacterium tuberculosis inhibitors

Rampogu¹,

Shaik²,

Kim³

et al. 2023

Heliyon

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“…Tobramycin and related molecules were also identified among candidate molecules for the S-RBD binding by drug repositioning analysis of drug molecules contained in the Drug ReposER database and molecular docking [79]. Pharmacophore-based drug repurposing analysis of the DrugBank compounds using docking screening and MD simulations revealed that framycetin, kanamycin, and tobramycin were among the promising candidate compounds to inhibit S protein and viral entry [80].…”

Section: Simulations Of the Sars-cov-2 Spike Trimers And Analysis Of ...mentioning

confidence: 99%

Exploring Binding Pockets in the Conformational States of the SARS-CoV-2 Spike Trimers for the Screening of Allosteric Inhibitors Using Molecular Simulations and Ensemble-Based Ligand Docking

Gupta,

Verkhivker

2024

IJMS

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Understanding mechanisms of allosteric regulation remains elusive for the SARS-CoV-2 spike protein, despite the increasing interest and effort in discovering allosteric inhibitors of the viral activity and interactions with the host receptor ACE2. The challenges of discovering allosteric modulators of the SARS-CoV-2 spike proteins are associated with the diversity of cryptic allosteric sites and complex molecular mechanisms that can be employed by allosteric ligands, including the alteration of the conformational equilibrium of spike protein and preferential stabilization of specific functional states. In the current study, we combine conformational dynamics analysis of distinct forms of the full-length spike protein trimers and machine-learning-based binding pocket detection with the ensemble-based ligand docking and binding free energy analysis to characterize the potential allosteric binding sites and determine structural and energetic determinants of allosteric inhibition for a series of experimentally validated allosteric molecules. The results demonstrate a good agreement between computational and experimental binding affinities, providing support to the predicted binding modes and suggesting key interactions formed by the allosteric ligands to elicit the experimentally observed inhibition. We establish structural and energetic determinants of allosteric binding for the experimentally known allosteric molecules, indicating a potential mechanism of allosteric modulation by targeting the hinges of the inter-protomer movements and blocking conformational changes between the closed and open spike trimer forms. The results of this study demonstrate that combining ensemble-based ligand docking with conformational states of spike protein and rigorous binding energy analysis enables robust characterization of the ligand binding modes, the identification of allosteric binding hotspots, and the prediction of binding affinities for validated allosteric modulators, which is consistent with the experimental data. This study suggested that the conformational adaptability of the protein allosteric sites and the diversity of ligand bound conformations are both in play to enable efficient targeting of allosteric binding sites and interfere with the conformational changes.

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Pharmacophore Modelling-Based Drug Repurposing Approaches for SARS-CoV-2 Therapeutics

Cited by 23 publications

References 39 publications

Toward Unified AI Drug Discovery with Multimodal Knowledge

Toward Unified AI Drug Discovery with Multimodal Knowledge

Explicit molecular dynamics simulation studies to discover novel natural compound analogues as Mycobacterium tuberculosis inhibitors

Exploring Binding Pockets in the Conformational States of the SARS-CoV-2 Spike Trimers for the Screening of Allosteric Inhibitors Using Molecular Simulations and Ensemble-Based Ligand Docking

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