AIMC Topic: Peptides, Cyclic

Clear Filters Showing 1 to 10 of 25 articles

Ai-driven de novo design of customizable membrane permeable cyclic peptides.

Journal of computer-aided molecular design Aug 9, 2025
Cyclic peptides, prized for their remarkable bioactivity and stability, hold great promise across various fields. Yet, designing membrane-penetrating bioactive cyclic peptides via traditional methods is complex and resource-intensive. To address this...
Artificial Intelligence Drug Design Peptides, Cyclic Cell Membrane Permeability Cell-Penetrating Peptides Molecular Dynamics Simulation Amino Acid Sequence
View on PubMed DOI

Bulk Measurement of Membrane Permeability for Random Cyclic Peptides in Living Cells to Guide Drug Development.

Angewandte Chemie (International ed. in English) Jun 1, 2025
Cyclic peptides are attractive for drug discovery due to their excellent binding properties and the potential to cross cell membranes. However, by far, not all cyclic peptides are cell permeable, and measuring or predicting their membrane permeabilit...
Peptides, Cyclic Cell Membrane Permeability Humans Drug Development
View on PubMed DOI

Cyclic peptide structure prediction and design using AlphaFold2.

Nature communications May 21, 2025
Small cyclic peptides have gained significant traction as a therapeutic modality; however, the development of deep learning methods for accurately designing such peptides has been slow, mostly due to the lack of sufficiently large training sets. Here...
Proto-Oncogene Proteins c-mdm2 Kelch-Like ECH-Associated Protein 1 Crystallography, X-Ray Protein Conformation Computational Biology Models, Molecular Amino Acid Sequence Peptides, Cyclic Drug Design Deep Learning Humans
View on PubMed DOI

MultiCycPermea: accurate and interpretable prediction of cyclic peptide permeability using a multimodal image-sequence model.

BMC biology Feb 27, 2025
BACKGROUND: Cyclic peptides, known for their high binding affinity and low toxicity, show potential as innovative drugs for targeting "undruggable" proteins. However, their therapeutic efficacy is often hindered by poor membrane permeability. Over th...
Peptides, Cyclic Deep Learning Permeability
View on PubMed DOI

CPconf_score: A Deep Learning Free Energy Function Trained Using Molecular Dynamics Data for Cyclic Peptides.

Journal of chemical theory and computation Jan 13, 2025
Accurate structural feature characterization of cyclic peptides (CPs), especially those with less than 10 residues and -peptide bonds, is challenging but important for the rational design of bioactive peptides. In this study, we performed high-temper...
Peptides, Cyclic Deep Learning Protein Conformation Thermodynamics Molecular Dynamics Simulation
View on PubMed DOI

Investigating Ligand-Mediated Conformational Dynamics of Pre-miR21: A Machine-Learning-Aided Enhanced Sampling Study.

Journal of chemical information and modeling Nov 11, 2024
MicroRNAs (miRs) are short, noncoding RNA strands that regulate the activity of mRNAs by affecting the repression of protein translation, and their dysregulation has been implicated in several pathologies. miR21 in particular has been implicated in t...
Molecular Dynamics Simulation Ligands Peptides, Cyclic Nucleic Acid Conformation Machine Learning MicroRNAs Humans RNA Precursors Ribonuclease III
View on PubMed DOI

Machine learning, network pharmacology, and molecular dynamics reveal potent cyclopeptide inhibitors against dengue virus proteins.

Molecular diversity Sep 4, 2024
The dengue virus is a major global health hazard responsible for an estimated 390 million diseases yearly. This study focused on identifying cyclopeptide inhibitors for envelope structural proteins E, NS1, NS3, and NS5. Additionally, 5579 cyclopeptid...
Machine Learning Thermodynamics Viral Proteins Dengue Virus Peptides, Cyclic Quantitative Structure-Activity Relationship Protein Binding Viral Nonstructural Proteins Molecular Docking Simulation Molecular Dynamics Simulation Antiviral Agents
View on PubMed DOI

CREMP: Conformer-rotamer ensembles of macrocyclic peptides for machine learning.

Scientific data Aug 9, 2024
Computational and machine learning approaches to model the conformational landscape of macrocyclic peptides have the potential to enable rational design and optimization. However, accurate, fast, and scalable methods for modeling macrocycle geometrie...
Macrocyclic Compounds Peptides Peptides, Cyclic Machine Learning Protein Conformation
View on PubMed DOI

PepExplainer: An explainable deep learning model for selection-based macrocyclic peptide bioactivity prediction and optimization.

European journal of medicinal chemistry Jun 25, 2024
Macrocyclic peptides possess unique features, making them highly promising as a drug modality. However, evaluating their bioactivity through wet lab experiments is generally resource-intensive and time-consuming. Despite advancements in artificial in...
Molecular Structure Humans Peptides, Cyclic Peptides Macrocyclic Compounds Dose-Response Relationship, Drug Deep Learning Structure-Activity Relationship
View on PubMed DOI

Multi_CycGT: A Deep Learning-Based Multimodal Model for Predicting the Membrane Permeability of Cyclic Peptides.

Journal of medicinal chemistry Jan 25, 2024
Cyclic peptides are gaining attention for their strong binding affinity, low toxicity, and ability to target "undruggable" proteins; however, their therapeutic potential against intracellular targets is constrained by their limited membrane permeabil...
Cell Membrane Permeability Peptides, Cyclic Deep Learning Permeability Chemistry, Pharmaceutical
View on PubMed DOI
Popular Topics
Recent Journals