AIMC Topic: Models, Molecular

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Generative artificial intelligence performs rudimentary structural biology modeling.

Scientific reports Aug 21, 2024
Natural language-based generative artificial intelligence (AI) has become increasingly prevalent in scientific research. Intriguingly, capabilities of generative pre-trained transformer (GPT) language models beyond the scope of natural language tasks...
Amino Acids Protein Conformation Coronavirus 3C Proteases SARS-CoV-2 Models, Molecular Artificial Intelligence COVID-19 Humans
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Chemical analogue based drug design for cancer treatment targeting PI3K: integrating machine learning and molecular modeling.

Molecular diversity Aug 17, 2024
Cancer is a generic term for a group of disorders defined by uncontrolled cell growth and the potential to invade or spread to other parts of the body. Gene and epigenetic alterations disrupt normal cellular control, leading to abnormal cell prolifer...
Phosphoinositide-3 Kinase Inhibitors Class Ib Phosphatidylinositol 3-Kinase Ligands Protein Kinase Inhibitors Humans Antineoplastic Agents Protein Binding Phosphatidylinositol 3-Kinases Molecular Dynamics Simulation Models, Molecular Molecular Docking Simulation Drug Design Machine Learning Neoplasms
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MCNN_MC: Computational Prediction of Mitochondrial Carriers and Investigation of Bongkrekic Acid Toxicity Using Protein Language Models and Convolutional Neural Networks.

Journal of chemical information and modeling Aug 12, 2024
Mitochondrial carriers (MCs) are essential proteins that transport metabolites across mitochondrial membranes and play a critical role in cellular metabolism. ADP/ATP (adenosine diphosphate/adenosine triphosphate) is one of the most important carrier...
Mitochondrial ADP, ATP Translocases Mitochondria Bongkrekic Acid Humans Neural Networks, Computer Computational Biology Models, Molecular
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High-Throughput Screening and Prediction of Nucleophilicity of Amines Using Machine Learning and DFT Calculations.

Journal of chemical information and modeling Aug 8, 2024
Nucleophilic index () as a significant parameter plays a crucial role in screening of amine catalysts. Indeed, the quantity and variety of amines are extensive. However, only limited amines exhibit an value exceeding 4.0 eV, rendering them potential...
Quantitative Structure-Activity Relationship Amines Models, Molecular Machine Learning High-Throughput Screening Assays Density Functional Theory
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Boosting the performance of molecular property prediction via graph-text alignment and multi-granularity representation enhancement.

Journal of molecular graphics & modelling Aug 5, 2024
Deep learning is playing an increasingly important role in accurate prediction of molecular properties. Prior to being processed by a deep learning model, a molecule is typically represented in the form of a text or a graph. While some methods attemp...
Deep Learning Models, Molecular Algorithms
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Geometric deep learning of protein-DNA binding specificity.

Nature methods Aug 5, 2024
Predicting protein-DNA binding specificity is a challenging yet essential task for understanding gene regulation. Protein-DNA complexes usually exhibit binding to a selected DNA target site, whereas a protein binds, with varying degrees of binding sp...
DNA-Binding Proteins Binding Sites Protein Binding DNA Models, Molecular Deep Learning Computational Biology
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Evolutionary Probability and Stacked Regressions Enable Data-Driven Protein Engineering with Minimized Experimental Effort.

Journal of chemical information and modeling Aug 1, 2024
Protein engineering through directed evolution and (semi)rational approaches is routinely applied to optimize protein properties for a broad range of applications in industry and academia. The multitude of possible variants, combined with limited scr...
Models, Molecular Probability Protein Engineering Machine Learning Proteins
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AI-Driven Deep Learning Techniques in Protein Structure Prediction.

International journal of molecular sciences Aug 1, 2024
Protein structure prediction is important for understanding their function and behavior. This review study presents a comprehensive review of the computational models used in predicting protein structure. It covers the progression from established pr...
Artificial Intelligence Proteins Models, Molecular Protein Conformation Deep Learning Computational Biology
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Progress in the application of artificial intelligence in molecular generation models based on protein structure.

European journal of medicinal chemistry Jul 31, 2024
The molecular generation models based on protein structures represent a cutting-edge research direction in artificial intelligence-assisted drug discovery. This article aims to comprehensively summarize the research methods and developments by analyz...
Models, Molecular Drug Discovery Humans Protein Conformation Proteins Artificial Intelligence
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Neural network extrapolation to distant regions of the protein fitness landscape.

Nature communications Jul 30, 2024
Machine learning (ML) has transformed protein engineering by constructing models of the underlying sequence-function landscape to accelerate the discovery of new biomolecules. ML-guided protein design requires models, trained on local sequence-functi...
Models, Molecular Bacterial Proteins Neural Networks, Computer Immunoglobulin G Protein Binding Machine Learning Protein Engineering
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