AIMC Topic: 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 Molecular Structure Ligands Protein Binding Models, Theoretical Models, Molecular Artificial Intelligence Machine Learning Drug Design
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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 ...
Drug Repositioning COVID-19 Drug Treatment Animals Structure-Activity Relationship Humans Host-Pathogen Interactions COVID-19 Molecular Structure Drug Discovery SARS-CoV-2 Antiviral Agents Artificial Intelligence Machine Learning
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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 Development Molecular Structure Drug Interactions Amino Acid Sequence Neural Networks, Computer
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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 Machine Learning Algorithms Models, Molecular Molecular Structure Computer Graphics Drug Discovery Drug Development 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...
Computer Simulation Drug Design Drug Discovery Neural Networks, Computer Artificial Intelligence Algorithms Models, Chemical Organic Chemicals Computer Graphics Quantitative Structure-Activity Relationship Computational Biology Molecular Structure
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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 ...
Proteins Humans Databases, Protein Models, Molecular Drug Discovery Ligands Machine Learning Protein Binding Molecular Structure Binding, Competitive Quantitative Structure-Activity Relationship Algorithms Computational Biology Pharmaceutical Preparations Drug Design
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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 ...
Pharmaceutical Preparations Drug Discovery Ligands Deep Learning Molecular Conformation Protein Binding Humans Models, Molecular Computational Biology Software Neural Networks, Computer Proteins Molecular Structure Algorithms Reproducibility of Results
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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 Ligands Crystallization Drug Discovery Deep Learning Protein Binding Molecular Structure Drug Evaluation, Preclinical Databases, Protein Receptors, G-Protein-Coupled Humans Algorithms Molecular Docking Simulation
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DeepAtomicCharge: a new graph convolutional network-based architecture for accurate prediction of atomic charges.

Briefings in bioinformatics May 20, 2021
Atomic charges play a very important role in drug-target recognition. However, computation of atomic charges with high-level quantum mechanics (QM) calculations is very time-consuming. A number of machine learning (ML)-based atomic charge prediction ...
Neural Networks, Computer Molecular Structure Quantum Theory Machine Learning Drug Design
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Beware of the generic machine learning-based scoring functions in structure-based virtual screening.

Briefings in bioinformatics May 20, 2021
Machine learning-based scoring functions (MLSFs) have attracted extensive attention recently and are expected to be potential rescoring tools for structure-based virtual screening (SBVS). However, a major concern nowadays is whether MLSFs trained for...
Molecular Docking Simulation Drug Discovery Machine Learning User-Computer Interface Protein Binding Molecular Structure Datasets as Topic
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