ArticleScientific reports2021
Using molecular dynamics simulations to prioritize and understand AI-generated cell penetrating peptides.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Closed-Loop Multi-Objective Optimization for Receptor-Selective Cell-Penetrating Peptide Design.Journal of chemical information and modeling · 2026Article
- Echinocandins have an alternative mode of action on biomimetic membranes that is not directly related to the functioning of (1,3) beta-glucan synthase.Cell death discovery · 2026Article
- Discovering Plastic-Binding Peptides with Favorable Affinity, Water Solubility, and Binding Specificity Through Deep Learning and Biophysical Modeling.bioRxiv : the preprint server for biology · 2026Article
- Artificial intelligence for drug delivery: Yesterday, today and tomorrow.Acta pharmaceutica Sinica. B · 2026Review
- Advances in molecular dynamics approaches for investigating cell-penetrating peptides.Biophysical reviews · 2026Review
- Diffusion Models at the Drug Discovery Frontier: A Review on Generating Small Molecules Versus Therapeutic Peptides.Biology · 2025Review
- GPepT: A Foundation Language Model for Peptidomimetics Incorporating Noncanonical Amino Acids.ACS medicinal chemistry letters · 2025Article
- Neural Network Models for Prediction of Biological Activity using Molecular Dynamics Data: A Case of Photoswitchable Peptides.Molecular informatics · 2025Article
- Molecular Modelling in Bioactive Peptide Discovery and Characterisation.Biomolecules · 2025Review
- Therapeutic peptide development revolutionized: Harnessing the power of artificial intelligence for drug discovery.Heliyon · 2024Review
- Extensive antibody search with whole spectrum black-box optimization.Scientific reports · 2024Article
- Screening, optimization, and ADMET evaluation of HCJ007 for pancreatic cancer treatment through active learning and dynamics simulation.Frontiers in chemistry · 2024Article
- Molecular Dynamics Simulations of Drug-Conjugated Cell-Penetrating Peptides.Pharmaceuticals (Basel, Switzerland) · 2023Article
- APL@voro-interactive visualization and analysis of cell membrane simulations.Bioinformatics (Oxford, England) · 2023Article
- Funneling modulatory peptide design with generative models: Discovery and characterization of disruptors of calcineurin protein-protein interactions.PLoS computational biology · 2023Article
- The Advantage of Using Immunoinformatic Tools on Vaccine Design and Development for Coronavirus.Vaccines · 2022Review
- Deep generative models for peptide design.Digital discovery · 2022Review
- The role of cell-penetrating peptides in potential anti-cancer therapy.Clinical and translational medicine · 2022Review
- Design of Membrane Active Peptides Considering Multi-Objective Optimization for Biomedical Application.Membranes · 2022Review
- Approaches for evaluation of novel CPP-based cargo delivery systems.Frontiers in pharmacology · 2022Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cell-penetrating peptides have important therapeutic applications in drug delivery, but the variety of known cell-penetrating peptides is still limited. With a promise to accelerate peptide development, artificial intelligence (AI) techniques including deep generative models are currently in spotlight. Scientists, however, are often overwhelmed by an excessive number of unannotated sequences generated by AI and find it difficult to obtain insights to prioritize them for experimental validation. To avoid this pitfall, we leverage molecular dynamics (MD) simulations to obtain mechanistic information to prioritize and understand AI-generated peptides. A mechanistic score of permeability is computed from five steered MD simulations starting from different initial structures predicted by homology modelling. To compensate for variability of predicted structures, the score is computed with sample variance penalization so that a peptide with consistent behaviour is highly evaluated. Our computational pipeline involving deep learning, homology modelling, MD simulations and synthesizability assessment generated 24 novel peptide sequences. The top-scoring peptide showed a consistent pattern of conformational change in all simulations regardless of initial structures. As a result of wet-lab-experiments, our peptide showed better permeability and weaker toxicity in comparison to a clinically used peptide, TAT. Our result demonstrates how MD simulations can support de novo peptide design by providing mechanistic information supplementing statistical inference.
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Registered trials
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