AIMC Topic: Receptors, G-Protein-Coupled

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Leveraging Artificial Intelligence in GPCR Activation Studies: Computational Prediction Methods as Key Drivers of Knowledge.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2025
G protein-coupled receptors (GPCRs) are key molecules involved in cellular signaling and are attractive targets for pharmacological intervention. This chapter is designed to explore the range of algorithms used to predict GPCRs' activation states, wh...
Artificial Intelligence Algorithms Computational Biology Humans Receptors, G-Protein-Coupled Signal Transduction Databases, Protein Molecular Dynamics Simulation Ligands
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PRECOGx: exploring GPCR signaling mechanisms with deep protein representations.

Nucleic acids research Jul 5, 2022
In this study we show that protein language models can encode structural and functional information of GPCR sequences that can be used to predict their signaling and functional repertoire. We used the ESM1b protein embeddings as features and the bind...
Heterotrimeric GTP-Binding Proteins Signal Transduction Receptors, G-Protein-Coupled Alternative Splicing Software Humans Computers beta-Arrestins Internet Machine Learning Genetic Predisposition to Disease
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DeepREAL: a deep learning powered multi-scale modeling framework for predicting out-of-distribution ligand-induced GPCR activity.

Bioinformatics (Oxford, England) Apr 28, 2022
MOTIVATION: Drug discovery has witnessed intensive exploration of predictive modeling of drug-target physical interactions over two decades. However, a critical knowledge gap needs to be filled for correlating drug-target interactions with clinical o...
Receptors, G-Protein-Coupled Amino Acid Sequence Ligands Drug Discovery Deep Learning
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Predicting Residence Time of GPCR Ligands with Machine Learning.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2022
Drug-target residence time, the duration of binding at a given protein target, has been shown in some protein families to be more significant for conferring efficacy than binding affinity. To carry out efficient optimization of residence time in drug...
Receptors, G-Protein-Coupled Kinetics Ligands Protein Binding Signal Transduction Humans Machine Learning
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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 Receptors, G-Protein-Coupled Molecular Structure Humans Drug Evaluation, Preclinical Protein Binding Algorithms Databases, Protein Drug Discovery Molecular Docking Simulation Deep Learning
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A combination of machine learning and infrequent metadynamics to efficiently predict kinetic rates, transition states, and molecular determinants of drug dissociation from G protein-coupled receptors.

The Journal of chemical physics Sep 28, 2020
Determining the drug-target residence time (RT) is of major interest in drug discovery given that this kinetic parameter often represents a better indicator of in vivo drug efficacy than binding affinity. However, obtaining drug-target unbinding rate...
Pharmaceutical Preparations Kinetics Molecular Dynamics Simulation Machine Learning Receptors, G-Protein-Coupled
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Classification of particle trajectories in living cells: Machine learning versus statistical testing hypothesis for fractional anomalous diffusion.

Physical review. E Sep 1, 2020
Single-particle tracking (SPT) has become a popular tool to study the intracellular transport of molecules in living cells. Inferring the character of their dynamics is important, because it determines the organization and functions of the cells. For...
Models, Biological Biological Transport Single Molecule Imaging GTP-Binding Proteins Diffusion Machine Learning Cell Survival Receptors, G-Protein-Coupled
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DeepFam: deep learning based alignment-free method for protein family modeling and prediction.

Bioinformatics (Oxford, England) Jul 1, 2018
MOTIVATION: A large number of newly sequenced proteins are generated by the next-generation sequencing technologies and the biochemical function assignment of the proteins is an important task. However, biological experiments are too expensive to cha...
Receptors, G-Protein-Coupled Sequence Analysis, Protein Software Deep Learning Proteins
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WDL-RF: predicting bioactivities of ligand molecules acting with G protein-coupled receptors by combining weighted deep learning and random forest.

Bioinformatics (Oxford, England) Jul 1, 2018
MOTIVATION: Precise assessment of ligand bioactivities (including IC50, EC50, Ki, Kd, etc.) is essential for virtual screening and lead compound identification. However, not all ligands have experimentally determined activities. In particular, many G...
Drug Evaluation, Preclinical Ligands Humans Deep Learning Animals Computational Biology Drug Discovery Receptors, G-Protein-Coupled
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Some Remarks on Prediction of Drug-Target Interaction with Network Models.

Current topics in medicinal chemistry Jan 1, 2017
System-level understanding of the relationships between drugs and targets is very important for enhancing drug research, especially for drug function repositioning. The experimental methods used to determine drug-target interactions are usually time-...
Pharmaceutical Preparations Algorithms Humans Ligands Ion Channels Molecular Docking Simulation Molecular Targeted Therapy Receptors, G-Protein-Coupled Neural Networks, Computer
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