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Peptides, Cyclic

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

BMC biology
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...

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

Journal of chemical theory and computation
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...

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

Journal of chemical information and modeling
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...

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

Scientific data
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...

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

European journal of medicinal chemistry
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...

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

Journal of medicinal chemistry
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...

Simplified and enhanced VCD analysis of cyclic peptides guided by artificial intelligence.

Physical chemistry chemical physics : PCCP
Cyclic peptides are privileged structures in medicinal chemistry; however, their solution-state structure characterization is difficult. Vibrational circular dichroism (VCD) spectroscopy is a powerful alternative to NMR, but requires challenging calc...

Training Neural Network Models Using Molecular Dynamics Simulation Results to Efficiently Predict Cyclic Hexapeptide Structural Ensembles.

Journal of chemical theory and computation
Cyclic peptides have emerged as a promising class of therapeutics. However, their design remains challenging, and many cyclic peptide drugs are simply natural products or their derivatives. Most cyclic peptides, including the current cyclic peptide ...

Broad-specificity ELISA with a heterogeneous strategy for sensitive detection of microcystins and nodularin.

Toxicon : official journal of the International Society on Toxinology
A highly sensitive and broadly specific competitive indirect enzyme-linked immunosorbent assay (ciELISA) method was developed for the simultaneous detection of nine microcystins (MCs) and nodularin (NOD) using MC-LR-keyhole limpet hemocyanin (KLH) fo...

A new cyclic lipopeptide isolated from Bacillus amyloliquefaciens HAB-2 and safety evaluation.

Pesticide biochemistry and physiology
Bacillus is the most widely studied biocontrol agent and has been extensively used in the development of biopesticides and fungicides. In this study, a new cyclic lipopeptide was isolated from Bacillus amyloliquefaciens HAB-2 by column chromatography...