AIMC Topic: Molecular Structure

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ComABAN: refining molecular representation with the graph attention mechanism to accelerate drug discovery.

Briefings in bioinformatics Sep 20, 2022
An unsolved challenge in developing molecular representation is determining an optimal method to characterize the molecular structure. Comprehension of intramolecular interactions is paramount toward achieving this goal. In this study, ComABAN, a new...
Machine Learning Drug Discovery Molecular Structure Molecular Dynamics Simulation Neural Networks, Computer
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ABC-Net: a divide-and-conquer based deep learning architecture for SMILES recognition from molecular images.

Briefings in bioinformatics Mar 10, 2022
Structural information for chemical compounds is often described by pictorial images in most scientific documents, which cannot be easily understood and manipulated by computers. This dilemma makes optical chemical structure recognition (OCSR) an ess...
Neural Networks, Computer Deep Learning Molecular Structure
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Molecular persistent spectral image (Mol-PSI) representation for machine learning models in drug design.

Briefings in bioinformatics Jan 17, 2022
Artificial intelligence (AI)-based drug design has great promise to fundamentally change the landscape of the pharmaceutical industry. Even though there are great progress from handcrafted feature-based machine learning models, 3D convolutional neura...
Neural Networks, Computer Artificial Intelligence Machine Learning Models, Molecular Molecular Structure Ligands Drug Design Protein Binding Models, Theoretical
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Fighting COVID-19 with Artificial Intelligence.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2022
The development of vaccines for the treatment of COVID-19 is paving the way for new hope. Despite this, the risk of the virus mutating into a vaccine-resistant variant still persists. As a result, the demand of efficacious drugs to treat COVID-19 is ...
Humans Antiviral Agents Drug Repositioning Structure-Activity Relationship SARS-CoV-2 Artificial Intelligence Molecular Structure Animals Machine Learning COVID-19 Drug Treatment Drug Discovery COVID-19 Host-Pathogen Interactions
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Predicting Drug-Target Affinity Based on Recurrent Neural Networks and Graph Convolutional Neural Networks.

Combinatorial chemistry & high throughput screening Jan 1, 2022
BACKGROUND: Drug development requires a lot of money and time, and the outcome of the challenge is unknown. So, there is an urgent need for researchers to find a new approach that can reduce costs. Therefore, the identification of drug-target interac...
Drug Interactions Amino Acid Sequence Neural Networks, Computer Drug Development Molecular Structure
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Utilizing graph machine learning within drug discovery and development.

Briefings in bioinformatics Nov 5, 2021
Graph machine learning (GML) is receiving growing interest within the pharmaceutical and biotechnology industries for its ability to model biomolecular structures, the functional relationships between them, and integrate multi-omic datasets - amongst...
Neural Networks, Computer Models, Molecular Machine Learning Drug Discovery Molecular Structure Algorithms Drug Development Computer Graphics Drug Repositioning
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Hyperbolic relational graph convolution networks plus: a simple but highly efficient QSAR-modeling method.

Briefings in bioinformatics Sep 2, 2021
Accurate predictions of druggability and bioactivities of compounds are desirable to reduce the high cost and time of drug discovery. After more than five decades of continuing developments, quantitative structure-activity relationship (QSAR) methods...
Organic Chemicals Computer Simulation Computational Biology Drug Design Models, Chemical Molecular Structure Neural Networks, Computer Drug Discovery Quantitative Structure-Activity Relationship Artificial Intelligence Computer Graphics Algorithms
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Persistent spectral hypergraph based machine learning (PSH-ML) for protein-ligand binding affinity prediction.

Briefings in bioinformatics Sep 2, 2021
Molecular descriptors are essential to not only quantitative structure activity/property relationship (QSAR/QSPR) models, but also machine learning based chemical and biological data analysis. In this paper, we propose persistent spectral hypergraph ...
Ligands Drug Design Quantitative Structure-Activity Relationship Drug Discovery Binding, Competitive Protein Binding Computational Biology Models, Molecular Humans Proteins Molecular Structure Machine Learning Databases, Protein Pharmaceutical Preparations Algorithms
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A spatial-temporal gated attention module for molecular property prediction based on molecular geometry.

Briefings in bioinformatics Sep 2, 2021
MOTIVATION: Geometry-based properties and characteristics of drug molecules play an important role in drug development for virtual screening in computational chemistry. The 3D characteristics of molecules largely determine the properties of the drug ...
Algorithms Reproducibility of Results Drug Discovery Deep Learning Protein Binding Computational Biology Humans Neural Networks, Computer Software Pharmaceutical Preparations Proteins Ligands Models, Molecular Molecular Conformation Molecular Structure
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Machine learning on ligand-residue interaction profiles to significantly improve binding affinity prediction.

Briefings in bioinformatics Sep 2, 2021
Structure-based virtual screenings (SBVSs) play an important role in drug discovery projects. However, it is still a challenge to accurately predict the binding affinity of an arbitrary molecule binds to a drug target and prioritize top ligands from ...
Protein Kinases Receptors, G-Protein-Coupled Ligands Humans Molecular Structure Crystallization Algorithms Drug Evaluation, Preclinical Molecular Docking Simulation Protein Binding Drug Discovery Databases, Protein Deep Learning
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