AIMC Topic: Models, Molecular

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Deep Learning Protocol for Predicting Full-Spectrum Infrared and Raman Spectra of Polypeptides and Proteins Using All-Atom Models.

The journal of physical chemistry letters Feb 18, 2025
Infrared (IR) spectroscopy and Raman spectroscopy are powerful tools for probing protein and peptide structures due to their capability to provide molecular fingerprints. As a popular spectral simulation method, the quantum chemistry (QC) calculation...
Spectrum Analysis, Raman Peptides Spectrophotometry, Infrared Proteins Deep Learning Models, Molecular Quantum Theory
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Identifying RNA-small Molecule Binding Sites Using Geometric Deep Learning with Language Models.

Journal of molecular biology Feb 15, 2025
RNAs are emerging as promising therapeutic targets, yet identifying small molecules that bind to them remains a significant challenge in drug discovery. This underscores the crucial role of computational modeling in predicting RNA-small molecule bind...
RNA Nucleic Acid Conformation Computational Biology Models, Molecular Binding Sites Small Molecule Libraries Drug Discovery Deep Learning
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Simpler Protein Domain Identification Using Spectral Clustering.

Proteins Feb 13, 2025
The decomposition of a biomolecular complex into domains is an important step to investigate biological functions and ease structure determination. A successful approach to do so is the SPECTRUS algorithm, which provides a segmentation based on spect...
Proteins Databases, Protein Models, Molecular Computational Biology Cluster Analysis Protein Conformation Sequence Alignment Sequence Analysis, Protein Algorithms Protein Domains Deep Learning Amino Acid Sequence
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Self-supervised machine learning methods for protein design improve sampling but not the identification of high-fitness variants.

Science advances Feb 12, 2025
Machine learning (ML) is changing the world of computational protein design, with data-driven methods surpassing biophysical-based methods in experimental success. However, they are most often reported as case studies, lack integration and standardiz...
Models, Molecular Mutation Protein Engineering Supervised Machine Learning Algorithms Computational Biology Proteins Machine Learning Software
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Recent advances and future challenges in predictive modeling of metalloproteins by artificial intelligence.

Molecules and cells Feb 10, 2025
Metal coordination is essential for structural/catalytic functions of metalloproteins that mediate a wide range of biological processes in living organisms. Advances in bioinformatics have significantly enhanced our understanding of metal-binding sit...
Binding Sites Metalloproteins Computational Biology Models, Molecular Humans Artificial Intelligence Metals
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Long-Range Electrostatics in Serine Proteases: Machine Learning-Driven Reaction Sampling Yields Insights for Enzyme Design.

Journal of chemical information and modeling Feb 10, 2025
Computational enzyme design is a promising technique for producing novel enzymes for industrial and clinical needs. A key challenge that this technique faces is to consistently achieve the desired activity. Fundamental studies of natural enzymes reve...
Serine Proteases Static Electricity Protein Engineering Biocatalysis Catalytic Domain Models, Molecular Quantum Theory Machine Learning
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Modeling Boltzmann-weighted structural ensembles of proteins using artificial intelligence-based methods.

Current opinion in structural biology Feb 8, 2025
This review highlights recent advances in AI-driven methods for generating Boltzmann-weighted structural ensembles, which are crucial for understanding biomolecular dynamics and drug discovery. With the rise of deep learning models such as AlphaFold2...
Artificial Intelligence Drug Discovery Models, Molecular Molecular Dynamics Simulation Protein Conformation Deep Learning Proteins
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Severe deviation in protein fold prediction by advanced AI: a case study.

Scientific reports Feb 8, 2025
Artificial intelligence (AI) and deep learning are making groundbreaking strides in protein structure prediction. AlphaFold is remarkable in this arena for its outstanding accuracy in modelling proteins fold based solely on their amino acid sequences...
Models, Molecular Protein Conformation Protein Folding Computational Biology Proteins Artificial Intelligence Deep Learning
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Advancing structure modeling from cryo-EM maps with deep learning.

Biochemical Society transactions Feb 7, 2025
Cryo-electron microscopy (cryo-EM) has revolutionized structural biology by enabling the determination of biomolecular structures that are challenging to resolve using conventional methods. Interpreting a cryo-EM map requires accurate modeling of the...
Cryoelectron Microscopy Models, Molecular Protein Conformation Deep Learning Proteins
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DFT and machine learning integration to predict efficiency of modified metal-free dyes in DSSCs.

Journal of molecular graphics & modelling Feb 6, 2025
Power conversion efficiency (PCE) prediction in dye-sensitized solar cells (DSSCs) increasingly relies on computation and machine learning, lowering experimental demands and accelerating materials discovery. In this work we incorporated quantum-chemi...
Machine Learning Coloring Agents Density Functional Theory Solar Energy Models, Molecular
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