ArticleComputational and structural biotechnology journal2022
Prediction of GPCR activity using machine learning.
Article in Computational and structural biotechnology journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 36 citations in OpenAlex.
- Residue Interaction Network Reveals Allosteric Pathways Linking Orthosteric and Intracellular Sites in Class A GPCRs.Journal of chemical information and modeling · 2026Article
- Computer-aided structural modeling and drug discovery for G-protein-coupled receptors in the age of artificial intelligence.Current opinion in structural biology · 2026Review
- Integrated Computer-Aided Drug Design: Advances in GPCR Natural Ligand Discovery.Cell biochemistry and biophysics · 2026Review
- The Evolving Landscape of Protein Allostery: From Computational and Experimental Perspectives.Journal of molecular biology · 2025Review
- Protein Structure-Function Relationship: A Kernel-PCA Approach for Reaction Coordinate Identification.Journal of chemical theory and computation · 2025Article
- EnGCI: enhancing GPCR-compound interaction prediction via large molecular models and KAN network.BMC biology · 2025Article
- Multi-Peptide: Multimodality Leveraged Language-Graph Learning of Peptide Properties.Journal of chemical information and modeling · 2025Article
- Leveraging Artificial Intelligence in GPCR Activation Studies: Computational Prediction Methods as Key Drivers of Knowledge.Methods in molecular biology (Clifton, N.J.) · 2025Article
- IDP-Bert: Predicting Properties of Intrinsically Disordered Proteins Using Large Language Models.The journal of physical chemistry. B · 2024Article
- GPCR-BERT: Interpreting Sequential Design of G Protein-Coupled Receptors Using Protein Language Models.Journal of chemical information and modeling · 2024Article
- Decrypting orphan GPCR drug discovery via multitask learning.Journal of cheminformatics · 2024Article
- Characterizing conformational states in GPCR structures using machine learning.Scientific reports · 2024Article
- Unveiling Switching Function of Amino Acids in Proteins Using a Machine Learning Approach.Journal of chemical theory and computation · 2023Article
- PeptideBERT: A Language Model Based on Transformers for Peptide Property Prediction.The journal of physical chemistry letters · 2023Article
- Activity Map and Transition Pathways of G Protein-Coupled Receptor Revealed by Machine Learning.Journal of chemical information and modeling · 2023Article
- Expression of olfactory receptor genes in non-olfactory tissues in the developing and adult zebrafish.Scientific reports · 2023Article
- Layer-Wise Relevance Analysis for Motif Recognition in the Activation Pathway of theInternational journal of molecular sciences · 2023Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
GPCRs are the target for one-third of the FDA-approved drugs, however; the development of new drug molecules targeting GPCRs is limited by the lack of mechanistic understanding of the GPCR structure-activity-function relationship. To modulate the GPCR activity with highly specific drugs and minimal side-effects, it is necessary to quantitatively describe the important structural features in the GPCR and correlate them to the activation state of GPCR. In this study, we developed 3 ML approaches to predict the conformation state of GPCR proteins. Additionally, we predict the activity level of GPCRs based on their structure. We leverage the unique advantages of each of the 3 ML approaches, interpretability of XGBoost, minimal feature engineering for 3D convolutional neural network, and graph representation of protein structure for graph neural network. By using these ML approaches, we are able to predict the activation state of GPCRs with high accuracy (91%-95%) and also predict the activation state of GPCRs with low error (MAE of 7.15-10.58). Furthermore, the interpretation of the ML approaches allows us to determine the importance of each of the features in distinguishing between the GPCRs conformations.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.