AIMC Topic: Models, Molecular

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Improved Protein Structure Prediction Using a New Multi-Scale Network and Homologous Templates.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) Oct 31, 2021
The accuracy of de novo protein structure prediction has been improved considerably in recent years, mostly due to the introduction of deep learning techniques. In this work, trRosettaX, an improved version of trRosetta for protein structure predicti...
Sequence Analysis, Protein Neural Networks, Computer Deep Learning Computational Biology Models, Molecular Datasets as Topic Protein Conformation
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The AlphaFold Database of Protein Structures: A Biologist's Guide.

Journal of molecular biology Oct 29, 2021
AlphaFold, the deep learning algorithm developed by DeepMind, recently released the three-dimensional models of the whole human proteome to the scientific community. Here we discuss the advantages, limitations and the still unsolved challenges of the...
Protein Conformation Protein Folding Molecular Biology Computational Biology Proteome Models, Molecular Humans Algorithms Databases, Factual Deep Learning
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Disease variant prediction with deep generative models of evolutionary data.

Nature Oct 27, 2021
Quantifying the pathogenicity of protein variants in human disease-related genes would have a marked effect on clinical decisions, yet the overwhelming majority (over 98%) of these variants still have unknown consequences. In principle, computational...
Genetic Predisposition to Disease Evolution, Molecular Proteins Humans Phenotype Biological Assay Genetic Variation Disease Unsupervised Machine Learning Models, Molecular Bayes Theorem Selection, Genetic Genetic Fitness
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Quantum Artificial Neural Network Approach to Derive a Highly Predictive 3D-QSAR Model for Blood-Brain Barrier Passage.

International journal of molecular sciences Oct 12, 2021
A successful passage of the blood-brain barrier (BBB) is an essential prerequisite for the drug molecules designed to act on the central nervous system. The logarithm of blood-brain partitioning (LogBB) has served as an effective index of molecular B...
Biological Transport Blood-Brain Barrier Quantum Theory Neural Networks, Computer Models, Molecular Quantitative Structure-Activity Relationship Pharmaceutical Preparations
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MANORAA: A machine learning platform to guide protein-ligand design by anchors and influential distances.

Structure (London, England : 1993) Oct 5, 2021
The MANORAA platform uses structure-based approaches to provide information on drug design originally derived from mapping tens of thousands of amino acids on a grid. In-depth analyses of the pockets, frequently occurring atoms, influential distances...
Humans Protein Binding Machine Learning Computational Biology Pandemics COVID-19 Ligands Proteins Databases, Protein SARS-CoV-2 Crystallography, X-Ray Trimethoprim Models, Molecular Drug Design Tetrahydrofolate Dehydrogenase Protein Domains
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In silico formulation prediction of drug/cyclodextrin/polymer ternary complexes by machine learning and molecular modeling techniques.

Carbohydrate polymers Sep 29, 2021
Ternary cyclodextrin (CD) complexes (drug/CD/polymer) can effectively improve the solubility of water-insoluble drugs with large size than binary CD formulations. However, ternary formulations are screened by a trial-and-error approach, which is labo...
Drug Compounding Hydrocortisone Machine Learning Benzimidazoles Quinolones Models, Molecular Cyclodextrins Polymers
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XENet: Using a new graph convolution to accelerate the timeline for protein design on quantum computers.

PLoS computational biology Sep 27, 2021
Graph representations are traditionally used to represent protein structures in sequence design protocols in which the protein backbone conformation is known. This infrequently extends to machine learning projects: existing graph convolution algorith...
Models, Molecular Computer-Aided Design Neural Networks, Computer Machine Learning Protein Conformation Computational Biology Algorithms Computer Graphics Proteins Computers
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When homologous sequences meet structural decoys: Accurate contact prediction by tFold in CASP14-(tFold for CASP14 contact prediction).

Proteins Sep 23, 2021
In this paper, we report our tFold framework's performance on the inter-residue contact prediction task in the 14th Critical Assessment of protein Structure Prediction (CASP14). Our tFold framework seamlessly combines both homologous sequences and st...
Neural Networks, Computer Sequence Analysis, Protein Structural Homology, Protein Proteins Software Models, Molecular Reproducibility of Results Protein Folding Computational Biology
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Biomolecular simulation based machine learning models accurately predict sites of tolerability to the unnatural amino acid acridonylalanine.

Scientific reports Sep 15, 2021
The incorporation of unnatural amino acids (Uaas) has provided an avenue for novel chemistries to be explored in biological systems. However, the successful application of Uaas is often hampered by site-specific impacts on protein yield and solubilit...
Protein Biosynthesis Models, Molecular Amino Acids Machine Learning Molecular Conformation Proteins Structure-Activity Relationship
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A deep-learning framework for multi-level peptide-protein interaction prediction.

Nature communications Sep 15, 2021
Peptide-protein interactions are involved in various fundamental cellular functions and their identification is crucial for designing efficacious peptide therapeutics. Recently, a number of computational methods have been developed to predict peptide...
Computational Biology Proteins Models, Molecular Protein Domains Binding Sites Peptides Deep Learning Protein Binding Algorithms Reproducibility of Results
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