AIMC Topic: Models, Molecular

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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...
Binding Sites DNA-Binding Proteins Protein Binding Computational Biology DNA Models, Molecular Deep Learning
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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...
Probability Proteins Machine Learning Protein Engineering Models, Molecular
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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...
Computational Biology Proteins Artificial Intelligence Deep Learning Protein Conformation Models, Molecular
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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...
Proteins Artificial Intelligence Models, Molecular Drug Discovery Protein Conformation Humans
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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...
Immunoglobulin G Protein Binding Neural Networks, Computer Machine Learning Protein Engineering Models, Molecular Bacterial Proteins
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Context-aware geometric deep learning for protein sequence design.

Nature communications Jul 25, 2024
Protein design and engineering are evolving at an unprecedented pace leveraging the advances in deep learning. Current models nonetheless cannot natively consider non-protein entities within the design process. Here, we introduce a deep learning appr...
Protein Conformation Protein Engineering Deep Learning Models, Molecular Amino Acid Sequence Proteins
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EnzyACT: A Novel Deep Learning Method to Predict the Impacts of Single and Multiple Mutations on Enzyme Activity.

Journal of chemical information and modeling Jul 22, 2024
Enzyme engineering involves the customization of enzymes by introducing mutations to expand the application scope of natural enzymes. One limitation of that is the complex interaction between two key properties, activity and stability, where the enha...
Deep Learning Models, Molecular Protein Engineering Enzymes Mutation
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δ-Conotoxin Structure Prediction and Analysis through Large-Scale Comparative and Deep Learning Modeling Approaches.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) Jul 21, 2024
The δ-conotoxins, a class of peptides produced in the venom of cone snails, are of interest due to their ability to inhibit the inactivation of voltage-gated sodium channels causing paralysis and other neurological responses, but difficulties in thei...
Conus Snail Conotoxins Algorithms Animals Deep Learning Models, Molecular
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3DReact: Geometric Deep Learning for Chemical Reactions.

Journal of chemical information and modeling Jul 15, 2024
Geometric deep learning models, which incorporate the relevant molecular symmetries within the neural network architecture, have considerably improved the accuracy and data efficiency of predictions of molecular properties. Building on this success, ...
Deep Learning Models, Molecular Models, Chemical Neural Networks, Computer
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Structure to Property: Chemical Element Embeddings for Predicting Electronic Properties of Crystals.

Journal of chemical information and modeling Jul 15, 2024
We present a new general-purpose machine learning model that is able to predict a variety of crystal properties, including Fermi level energy and band gap, as well as spectral ones such as electronic densities of states. The model is based on atomic ...
Electrons Crystallization Models, Molecular Machine Learning
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