AIMC Topic: Models, Molecular

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Machine Learning of Three-Dimensional Protein Structures to Predict the Functional Impacts of Genome Variation.

Journal of chemical information and modeling Apr 18, 2024
Research in the human genome sciences generates a substantial amount of genetic data for hundreds of thousands of individuals, which concomitantly increases the number of variants of unknown significance (VUS). Bioinformatic analyses can successfully...
Genome, Human Protein Conformation NF-E2-Related Factor 2 Proto-Oncogene Proteins c-myc Humans Models, Molecular Genetic Variation Machine Learning Computational Biology
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Encoding the space of protein-protein binding interfaces by artificial intelligence.

Computational biology and chemistry Apr 18, 2024
The physical interactions between proteins are largely determined by the structural properties at their binding interfaces. It was found that the binding interfaces in distinctive protein complexes are highly similar. The structural properties underl...
Models, Molecular Protein Binding Artificial Intelligence Proteins Protein Conformation Binding Sites
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Protein Engineering with Lightweight Graph Denoising Neural Networks.

Journal of chemical information and modeling Apr 17, 2024
Protein engineering faces challenges in finding optimal mutants from a massive pool of candidate mutants. In this study, we introduce a deep-learning-based data-efficient fitness prediction tool to steer protein engineering. Our methodology establish...
Deep Learning Proteins Neural Networks, Computer Protein Conformation Protein Engineering Models, Molecular Mutation
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Apprehensions and emerging solutions in ML-based protein structure prediction.

Current opinion in structural biology Apr 16, 2024
The three-dimensional structure of proteins determines their function in vital biological processes. Thus, when the structure is known, the molecular mechanism of protein function can be understood in more detail and obtained information utilized in ...
Proteins Models, Molecular Machine Learning Computational Biology Protein Conformation
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Modeling Zinc Complexes Using Neural Networks.

Journal of chemical information and modeling Apr 8, 2024
Understanding the energetic landscapes of large molecules is necessary for the study of chemical and biological systems. Recently, deep learning has greatly accelerated the development of models based on quantum chemistry, making it possible to build...
Coordination Complexes Thermodynamics Quantum Theory Molecular Conformation Models, Molecular Neural Networks, Computer Molecular Dynamics Simulation Zinc
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ALDELE: All-Purpose Deep Learning Toolkits for Predicting the Biocatalytic Activities of Enzymes.

Journal of chemical information and modeling Apr 4, 2024
Rapidly predicting enzyme properties for catalyzing specific substrates is essential for identifying potential enzymes for industrial transformations. The demand for sustainable production of valuable industry chemicals utilizing biological resources...
Biocatalysis Protein Conformation Enzymes Deep Learning Models, Molecular
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RNA3DB: A structurally-dissimilar dataset split for training and benchmarking deep learning models for RNA structure prediction.

Journal of molecular biology Mar 27, 2024
With advances in protein structure prediction thanks to deep learning models like AlphaFold, RNA structure prediction has recently received increased attention from deep learning researchers. RNAs introduce substantial challenges due to the sparser a...
Models, Molecular RNA Computational Biology Nucleic Acid Conformation Databases, Protein Software Deep Learning
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Sequential Contrastive and Deep Learning Models to Identify Selective Butyrylcholinesterase Inhibitors.

Journal of chemical information and modeling Mar 26, 2024
Butyrylcholinesterase (BChE) is a target of interest in late-stage Alzheimer's Disease (AD) where selective BChE inhibitors (BIs) may offer symptomatic treatment without the harsh side effects of acetylcholinesterase (AChE) inhibitors. In this study,...
Alzheimer Disease Acetylcholinesterase Humans Deep Learning Models, Molecular Cholinesterase Inhibitors Butyrylcholinesterase
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LGGA-MPP: Local Geometry-Guided Graph Attention for Molecular Property Prediction.

Journal of chemical information and modeling Mar 22, 2024
Molecular property prediction is a fundamental task of drug discovery. With the rapid development of deep learning, computational approaches for predicting molecular properties are experiencing increasing popularity. However, these existing methods o...
Deep Learning Models, Molecular Neural Networks, Computer Drug Discovery
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A suite of designed protein cages using machine learning and protein fragment-based protocols.

Structure (London, England : 1993) Mar 20, 2024
Designed protein cages and related materials provide unique opportunities for applications in biotechnology and medicine, but their creation remains challenging. Here, we apply computational approaches to design a suite of tetrahedrally symmetric, se...
Proteins Models, Molecular Hydrophobic and Hydrophilic Interactions Protein Conformation Machine Learning Molecular Docking Simulation Cryoelectron Microscopy
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