ArticleComputational intelligence and neuroscience2015
Clustering molecular dynamics trajectories for optimizing docking experiments.
Article in Computational intelligence and neuroscience, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
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- mdBIRCH for Fast, Scalable, Online Clustering of Molecular Dynamics Trajectories.Journal of chemical theory and computation · 2026Article
- Divide and Cluster: The DIVINE Framework for Deterministic Top-Down Analysis of Molecular Dynamics Trajectories.Journal of chemical information and modeling · 2026Article
- Multimodal Learning of Protein-Protein Interactions for Accurate Binding Affinity Prediction.ACS omega · 2025Article
- mdBIRCH for Fast, Scalable, Online Clustering of Molecular Dynamics Trajectories.bioRxiv : the preprint server for biology · 2025Article
- Structure-Based Insights into Stefin-Mediated Targeting of Fowlerpain-1: Towards Novel Therapeutics forPharmaceuticals (Basel, Switzerland) · 2025Article
- Computational Strategies for Broad Spectrum Venom Phospholipase AJournal of chemical information and modeling · 2025Article
- PPI-Graphomer: enhanced protein-protein affinity prediction using pretrained and graph transformer models.BMC bioinformatics · 2025Article
- k-Means NANI: An Improved Clustering Algorithm for Molecular Dynamics Simulations.Journal of chemical theory and computation · 2024Article
- k-Means NANI: an improved clustering algorithm for Molecular Dynamics simulations.bioRxiv : the preprint server for biology · 2024Article
- Interaction of sulfasalazine with outer surface of boron-nitride nanotube as a drug carrier in aqueous solution: insights from quantum mechanics and Monte Carlo simulation.BMC chemistry · 2023Article
- Embedding Nonrigid Solutes in an Averaged Environment: A Case Study on Rhodopsins.Journal of chemical theory and computation · 2023Article
- Localized Simple Multiple Kernel K-Means Clustering with Matrix-Induced Regularization.Computational intelligence and neuroscience · 2023Article
- One class classification for the detection of β2 adrenergic receptor agonists using single-ligand dynamic interaction data.Journal of cheminformatics · 2022Article
- Ligand-Based Virtual Screening and Molecular Docking of Benzimidazoles as Potential Inhibitors of Triosephosphate Isomerase Identified New Trypanocidal Agents.International journal of molecular sciences · 2022Article
- Common Structural Pattern for Flecainide Binding in Atrial-Selective KPharmaceutics · 2022Article
- Benchmarking ensemble docking methods in D3R Grand Challenge 4.Journal of computer-aided molecular design · 2022Article
- Machine learning methods for protein-protein binding affinity prediction in protein design.Frontiers in bioinformatics · 2022Article
- Study of tyramine-binding mechanism and insecticidal activity of oil extracted from Eucalyptus againstFrontiers in chemistry · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Molecular dynamics simulations of protein receptors have become an attractive tool for rational drug discovery. However, the high computational cost of employing molecular dynamics trajectories in virtual screening of large repositories threats the feasibility of this task. Computational intelligence techniques have been applied in this context, with the ultimate goal of reducing the overall computational cost so the task can become feasible. Particularly, clustering algorithms have been widely used as a means to reduce the dimensionality of molecular dynamics trajectories. In this paper, we develop a novel methodology for clustering entire trajectories using structural features from the substrate-binding cavity of the receptor in order to optimize docking experiments on a cloud-based environment. The resulting partition was selected based on three clustering validity criteria, and it was further validated by analyzing the interactions between 20 ligands and a fully flexible receptor (FFR) model containing a 20 ns molecular dynamics simulation trajectory. Our proposed methodology shows that taking into account features of the substrate-binding cavity as input for the k-means algorithm is a promising technique for accurately selecting ensembles of representative structures tailored to a specific ligand.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.