AIMC Topic: Models, Molecular

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A Simple Way to Incorporate Target Structural Information in Molecular Generative Models.

Journal of chemical information and modeling Jun 15, 2023
Deep learning generative models are now being applied in various fields including drug discovery. In this work, we propose a novel approach to include target 3D structural information in molecular generative models for structure-based drug design. Th...
Neural Networks, Computer Proteins Models, Molecular Drug Discovery Drug Design
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Residue-level error detection in cryoelectron microscopy models.

Structure (London, England : 1993) May 29, 2023
Building accurate protein models into moderate resolution (3-5 Å) cryoelectron microscopy (cryo-EM) maps is challenging and error prone. We have developed MEDIC (Model Error Detection in Cryo-EM), a robust statistical model that identifies local back...
Cryoelectron Microscopy Protein Conformation Machine Learning Models, Molecular Proteins
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ImmuneBuilder: Deep-Learning models for predicting the structures of immune proteins.

Communications biology May 29, 2023
Immune receptor proteins play a key role in the immune system and have shown great promise as biotherapeutics. The structure of these proteins is critical for understanding their antigen binding properties. Here, we present ImmuneBuilder, a set of de...
Single-Domain Antibodies Receptors, Antigen, T-Cell Antibodies Models, Molecular Deep Learning
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Training Neural Network Models Using Molecular Dynamics Simulation Results to Efficiently Predict Cyclic Hexapeptide Structural Ensembles.

Journal of chemical theory and computation May 26, 2023
Cyclic peptides have emerged as a promising class of therapeutics. However, their design remains challenging, and many cyclic peptide drugs are simply natural products or their derivatives. Most cyclic peptides, including the current cyclic peptide ...
Neural Networks, Computer Machine Learning Peptides, Cyclic Protein Structure, Tertiary Molecular Dynamics Simulation Models, Molecular
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Molecular Generation with Reduced Labeling through Constraint Architecture.

Journal of chemical information and modeling May 15, 2023
In the past few years, a number of machine learning (ML)-based molecular generative models have been proposed for generating molecules with desirable properties, but they all require a large amount of label data of pharmacological and physicochemical...
Models, Molecular Machine Learning Neural Networks, Computer Drug Discovery
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A deep learning model for predicting optimal distance range in crosslinking mass spectrometry data.

Proteomics May 3, 2023
Macromolecular assemblies play an important role in all cellular processes. While there has recently been significant progress in protein structure prediction based on deep learning, large protein complexes cannot be predicted with these approaches. ...
Cross-Linking Reagents Models, Molecular Deep Learning Proteins Mass Spectrometry
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AI-Driven De Novo Design and Molecular Modeling for Discovery of Small-Molecule Compounds as Potential Drug Candidates Targeting SARS-CoV-2 Main Protease.

International journal of molecular sciences Apr 29, 2023
Over the past three years, significant progress has been made in the development of novel promising drug candidates against COVID-19. However, SARS-CoV-2 mutations resulting in the emergence of new viral strains that can be resistant to the drugs use...
Neural Networks, Computer Drug Discovery Artificial Intelligence Coronavirus 3C Proteases COVID-19 Drug Treatment Small Molecule Libraries Models, Molecular
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CryoRes: Local Resolution Estimation of Cryo-EM Density Maps by Deep Learning.

Journal of molecular biology Mar 24, 2023
Recent progress in cryo-EM research has ignited a revolution in biological macromolecule structure determination. Resolution is an essential parameter for quality assessment of a cryo-EM density map, and it is known that resolution varies in differen...
Algorithms Deep Learning Protein Conformation Cryoelectron Microscopy Models, Molecular Macromolecular Substances
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Combining Machine Learning with Physical Knowledge in Thermodynamic Modeling of Fluid Mixtures.

Annual review of chemical and biomolecular engineering Mar 21, 2023
Thermophysical properties of fluid mixtures are important in many fields of science and engineering. However, experimental data are scarce in this field, so prediction methods are vital. Different types of physical prediction methods are available, r...
Models, Molecular Machine Learning Thermodynamics
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Real-to-bin conversion for protein residue distances.

Computational biology and chemistry Feb 25, 2023
Protein Structure Prediction (PSP) has achieved significant progress lately. Prediction of inter-residue distances by machine learning and their exploitation during the conformational search is largely among the critical factors behind the progress. ...
Protein Conformation Proteins Algorithms Machine Learning Computational Biology Models, Molecular Databases, Protein
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