AIMC Topic: Molecular Conformation

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Neural-Network Scoring Functions Identify Structurally Novel Estrogen-Receptor Ligands.

Journal of chemical information and modeling Sep 4, 2015
The magnitude of the investment required to bring a drug to the market hinders medical progress, requiring hundreds of millions of dollars and years of research and development. Any innovation that improves the efficiency of the drug-discovery proces...
Neural Networks, Computer Databases, Factual Drug Discovery Protein Binding Computer Simulation Humans Ligands Molecular Conformation Receptors, Estrogen Estradiol Models, Biological
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Development of 3D-QSAR Model for Acetylcholinesterase Inhibitors Using a Combination of Fingerprint, Molecular Docking, and Structure-Based Pharmacophore Approaches.

Toxicological sciences : an official journal of the Society of Toxicology Jul 21, 2015
Acetylcholinesterase (AChE), a serine hydrolase vital for regulating the neurotransmitter acetylcholine in animals, has been used as a target for drugs and pesticides. With the increasing availability of AChE crystal structures, with or without ligan...
Nootropic Agents Binding Sites Peptide Mapping Ligands Acetylcholinesterase GPI-Linked Proteins Insecticides Catalytic Domain Molecular Conformation Quantitative Structure-Activity Relationship Cholinesterase Inhibitors Expert Systems Imaging, Three-Dimensional Computational Biology Nerve Tissue Proteins Models, Molecular Databases, Protein Animals Molecular Docking Simulation Humans Artificial Intelligence
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GTAM: a molecular pretraining model with geometric triangle awareness.

Bioinformatics (Oxford, England) Sep 2, 2024
MOTIVATION: Molecular representation learning is pivotal for advancing deep learning applications in quantum chemistry and drug discovery. Existing methods for molecular representation learning often fall short of fully capturing the intricate intera...
Deep Learning Models, Molecular Molecular Conformation Drug Discovery
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Integrating AlphaFold and deep learning for atomistic interpretation of cryo-EM maps.

Briefings in bioinformatics Sep 22, 2023
Interpretation of cryo-electron microscopy (cryo-EM) maps requires building and fitting 3D atomic models of biological molecules. AlphaFold-predicted models generate initial 3D coordinates; however, model inaccuracy and conformational heterogeneity o...
Models, Molecular Molecular Conformation Protein Conformation Deep Learning Cryoelectron Microscopy
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Modern semiempirical electronic structure methods and machine learning potentials for drug discovery: Conformers, tautomers, and protonation states.

The Journal of chemical physics Mar 28, 2023
Modern semiempirical electronic structure methods have considerable promise in drug discovery as universal "force fields" that can reliably model biological and drug-like molecules, including alternative tautomers and protonation states. Herein, we c...
Molecular Conformation Drug Discovery Isomerism Machine Learning
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Fusing 2D and 3D molecular graphs as unambiguous molecular descriptors for conformational and chiral stereoisomers.

Briefings in bioinformatics Jan 19, 2023
The rapid progress of machine learning (ML) in predicting molecular properties enables high-precision predictions being routinely achieved. However, many ML models, such as conventional molecular graph, cannot differentiate stereoisomers of certain t...
Machine Learning Molecular Conformation Stereoisomerism
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Accounting Conformational Dynamics into Structural Modeling Reflected by Cryo-EM with Deep Learning.

Combinatorial chemistry & high throughput screening Jan 1, 2023
With the continuous development of structural biology, the requirement for accurate threedimensional structures during functional modulation of biological macromolecules is increasing. Therefore, determining the dynamic structures of bio-macromolecul...
Molecular Conformation Cryoelectron Microscopy Algorithms Deep Learning Artificial Intelligence
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epitope3D: a machine learning method for conformational B-cell epitope prediction.

Briefings in bioinformatics Jan 17, 2022
The ability to identify antigenic determinants of pathogens, or epitopes, is fundamental to guide rational vaccine development and immunotherapies, which are particularly relevant for rapid pandemic response. A range of computational tools has been d...
Machine Learning Computational Biology Epitopes, B-Lymphocyte Support Vector Machine Molecular Conformation
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Co-evolutionary distance predictions contain flexibility information.

Bioinformatics (Oxford, England) Dec 22, 2021
MOTIVATION: Co-evolution analysis can be used to accurately predict residue-residue contacts from multiple sequence alignments. The introduction of machine-learning techniques has enabled substantial improvements in precision and a shift from predict...
Molecular Conformation Sequence Alignment Computational Biology Proteins Machine Learning
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Machine-learning scoring functions trained on complexes dissimilar to the test set already outperform classical counterparts on a blind benchmark.

Briefings in bioinformatics Nov 5, 2021
The superior performance of machine-learning scoring functions for docking has caused a series of debates on whether it is due to learning knowledge from training data that are similar in some sense to the test data. With a systematically revised met...
Ligands Molecular Conformation Models, Molecular Workflow Models, Statistical Regression Analysis Machine Learning Algorithms Reproducibility of Results Protein Binding Databases, Factual
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