AIMC Topic: Models, Molecular

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Machine learning differentiates enzymatic and non-enzymatic metals in proteins.

Nature communications Jun 17, 2021
Metalloenzymes are 40% of all enzymes and can perform all seven classes of enzyme reactions. Because of the physicochemical similarities between the active sites of metalloenzymes and inactive metal binding sites, it is challenging to differentiate b...
Algorithms Metals Databases, Protein Machine Learning Static Electricity Models, Molecular Binding Sites Catalysis Catalytic Domain Enzymes Metalloproteins
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Machine learning models to select potential inhibitors of acetylcholinesterase activity from SistematX: a natural products database.

Molecular diversity Jun 16, 2021
Alzheimer's disease is the most common form of dementia, representing 60-70% of dementia cases. The enzyme acetylcholinesterase (AChE) cleaves the ester bonds in acetylcholine and plays an important role in the termination of acetylcholine activity a...
Biological Products Structure-Activity Relationship Area Under Curve Molecular Dynamics Simulation Acetylcholinesterase Models, Molecular Workflow Molecular Docking Simulation Drug Discovery ROC Curve Humans Machine Learning Algorithms Cholinesterase Inhibitors Cheminformatics Databases, Pharmaceutical
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Applications of artificial intelligence to drug design and discovery in the big data era: a comprehensive review.

Molecular diversity Jun 10, 2021
Artificial intelligence (AI) renders cutting-edge applications in diverse sectors of society. Due to substantial progress in high-performance computing, the development of superior algorithms, and the accumulation of huge biological and chemical data...
Protein Interaction Mapping Protein Folding Databases, Pharmaceutical Models, Molecular Workflow Data Mining Drug Design Drug Discovery Big Data Proteins Structure-Activity Relationship Protein Binding Models, Theoretical Humans Artificial Intelligence Algorithms
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Machine learning models for classification tasks related to drug safety.

Molecular diversity Jun 10, 2021
In this review, we outline the current trends in the field of machine learning-driven classification studies related to ADME (absorption, distribution, metabolism and excretion) and toxicity endpoints from the past six years (2015-2021). The study fo...
Algorithms Drug Design Support Vector Machine Models, Molecular Humans Neural Networks, Computer Machine Learning Tissue Distribution Drug-Related Side Effects and Adverse Reactions ERG1 Potassium Channel Pharmacokinetics Quantitative Structure-Activity Relationship
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Machine learning analyses of antibody somatic mutations predict immunoglobulin light chain toxicity.

Nature communications Jun 10, 2021
In systemic light chain amyloidosis (AL), pathogenic monoclonal immunoglobulin light chains (LC) form toxic aggregates and amyloid fibrils in target organs. Prompt diagnosis is crucial to avoid permanent organ damage, but delayed diagnosis is common ...
Caenorhabditis elegans Immunoglobulin Light Chains Immunoglobulin Light-chain Amyloidosis Recombinant Proteins Databases, Genetic Antibodies Humans Models, Molecular Algorithms Gene Expression Amino Acid Sequence Machine Learning Animals Mutation
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Machine Learning Reveals the Critical Interactions for SARS-CoV-2 Spike Protein Binding to ACE2.

The journal of physical chemistry letters Jun 4, 2021
SARS-CoV and SARS-CoV-2 bind to the human ACE2 receptor in practically identical conformations, although several residues of the receptor-binding domain (RBD) differ between them. Herein, we have used molecular dynamics (MD) simulations, machine lear...
Angiotensin-Converting Enzyme 2 Machine Learning Models, Molecular Binding Sites Spike Glycoprotein, Coronavirus Humans Molecular Dynamics Simulation
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AlphaFold - A Personal Perspective on the Impact of Machine Learning.

Journal of molecular biology Jun 2, 2021
I outline how over my career as a protein scientist Machine Learning has impacted my area of science and one of my pastimes, chess, where there are some interesting parallels. In 1968, modelling of three-dimensional structures was initiated based on ...
Chymotrypsin Protein Folding Machine Learning Humans Software Protein Conformation Proteins Animals Trypsin Models, Molecular
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Statistical Learning from Single-Molecule Experiments: Support Vector Machines and Expectation-Maximization Approaches to Understanding Protein Unfolding Data.

The journal of physical chemistry. B Jun 2, 2021
Single-molecule force spectroscopy has become a powerful tool for the exploration of dynamic processes that involve proteins; yet, meaningful interpretation of the experimental data remains challenging. Owing to low signal-to-noise ratio, experimenta...
Proteins Models, Molecular Motivation Protein Unfolding Support Vector Machine
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Structure-based protein function prediction using graph convolutional networks.

Nature communications May 26, 2021
The rapid increase in the number of proteins in sequence databases and the diversity of their functions challenge computational approaches for automated function prediction. Here, we introduce DeepFRI, a Graph Convolutional Network for predicting pro...
Models, Biological Databases, Protein Amino Acid Sequence Protein Structure, Tertiary Deep Learning Structure-Activity Relationship Datasets as Topic Computational Biology Models, Molecular Proteins
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Protein model accuracy estimation empowered by deep learning and inter-residue distance prediction in CASP14.

Scientific reports May 25, 2021
The inter-residue contact prediction and deep learning showed the promise to improve the estimation of protein model accuracy (EMA) in the 13th Critical Assessment of Protein Structure Prediction (CASP13). To further leverage the improved inter-resid...
Caspases Protein Conformation Computational Biology Models, Molecular Deep Learning
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