AIMC Topic: Models, Molecular

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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 ...
Models, Molecular Protein Conformation Computational Biology Proteins Machine Learning
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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...
Molecular Conformation Coordination Complexes Thermodynamics Molecular Dynamics Simulation Quantum Theory Zinc Neural Networks, Computer Models, Molecular
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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 Computational Biology Software Databases, Protein RNA Deep Learning Nucleic Acid Conformation
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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,...
Acetylcholinesterase Cholinesterase Inhibitors Models, Molecular Alzheimer Disease Butyrylcholinesterase Humans Deep Learning
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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...
Drug Discovery Deep Learning Models, Molecular Neural Networks, Computer
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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...
Hydrophobic and Hydrophilic Interactions Protein Conformation Cryoelectron Microscopy Proteins Models, Molecular Machine Learning Molecular Docking Simulation
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Predictive Modelling in pharmacokinetics: from in-silico simulations to personalized medicine.

Expert opinion on drug metabolism & toxicology Mar 20, 2024
INTRODUCTION: Pharmacokinetic parameters assessment is a critical aspect of drug discovery and development, yet challenges persist due to limited training data. Despite advancements in machine learning and in-silico predictions, scarcity of data hamp...
Drug Design Drug Discovery Precision Medicine Reproducibility of Results Models, Molecular Pharmaceutical Preparations Models, Biological Pharmacogenetics Drug Development Humans Machine Learning Computer Simulation Artificial Intelligence Pharmacokinetics
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PMSPcnn: Predicting protein stability changes upon single point mutations with convolutional neural network.

Structure (London, England : 1993) Mar 19, 2024
Protein missense mutations and resulting protein stability changes are important causes for many human genetic diseases. However, the accurate prediction of stability changes due to mutations remains a challenging problem. To address this problem, we...
Humans Neural Networks, Computer Mutation, Missense Protein Stability Point Mutation Tumor Suppressor Protein p53 DNA Glycosylases Myoglobin Databases, Protein Models, Molecular
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PROTACable Is an Integrative Computational Pipeline of 3-D Modeling and Deep Learning To Automate the De Novo Design of PROTACs.

Journal of chemical information and modeling Mar 19, 2024
Proteolysis-targeting chimeras (PROTACs) that engage two biological targets at once are a promising technology in degrading clinically relevant protein targets. Since factors that influence the biological activities of PROTACs are more complex than t...
Proteolysis Targeting Chimera Ubiquitin-Protein Ligases Proteolysis Deep Learning Models, Molecular Drug Design
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