AIMC Topic: Cyclization

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Computational prediction of complex cationic rearrangement outcomes.

Nature Nov 15, 2023
Recent years have seen revived interest in computer-assisted organic synthesis. The use of reaction- and neural-network algorithms that can plan multistep synthetic pathways have revolutionized this field, including examples leading to advanced natur...
Neural Networks, Computer Algorithms Cyclization Chemistry Techniques, Synthetic Cations Solutions Terpenes Biological Products Knowledge Bases Reproducibility of Results
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Recapitulating the Binding Affinity of Nrf2 for KEAP1 in a Cyclic Heptapeptide, Guided by NMR, X-ray Crystallography, and Machine Learning.

Journal of the American Chemical Society Mar 8, 2021
Macrocycles, including macrocyclic peptides, have shown promise for targeting challenging protein-protein interactions (PPIs). One PPI of high interest is between Kelch-like ECH-Associated Protein-1 (KEAP1) and Nuclear Factor (Erythroid-derived 2)-li...
Kelch-Like ECH-Associated Protein 1 Protein Binding Recombinant Proteins Structure-Activity Relationship NF-E2-Related Factor 2 Fluorescence Polarization Amino Acid Motifs Mutagenesis, Site-Directed Crystallography, X-Ray Machine Learning Nuclear Magnetic Resonance, Biomolecular Cyclization Hydrogen Bonding Humans Peptides
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Perturbation-Theory and Machine Learning (PTML) Model for High-Throughput Screening of Parham Reactions: Experimental and Theoretical Studies.

Journal of chemical information and modeling Jun 27, 2018
Machine learning (ML) algorithms are gaining importance in the processing of chemical information and modeling of chemical reactivity problems. In this work, we have developed a perturbation-theory and machine learning (PTML) model combining perturba...
High-Throughput Screening Assays Linear Models Isoquinolines Lithium Machine Learning Molecular Structure Thermodynamics Algorithms Models, Chemical Cyclization Databases, Chemical Neural Networks, Computer Structure-Activity Relationship Organometallic Compounds
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NCPepFold: Accurate Prediction of Noncanonical Cyclic Peptide Structures via Cyclization Optimization with Multigranular Representation.

Journal of chemical theory and computation May 13, 2025
Artificial intelligence-based peptide structure prediction methods have revolutionized biomolecular science. However, restricting predictions to peptides composed solely of 20 natural amino acids significantly limits their practical application; as s...
Deep Learning Models, Molecular Protein Conformation Cyclization Amino Acids Peptides, Cyclic
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DNAcycP: a deep learning tool for DNA cyclizability prediction.

Nucleic acids research Apr 8, 2022
DNA mechanical properties play a critical role in every aspect of DNA-dependent biological processes. Recently a high throughput assay named loop-seq has been developed to quantify the intrinsic bendability of a massive number of DNA fragments simult...
Binding Sites Chromatin Saccharomyces cerevisiae Nucleosomes Cyclization Deep Learning Mice DNA Mammals Software Animals
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