ArticleComputational and structural biotechnology journal2023
Evaluating GPCR modeling and docking strategies in the era of deep learning-based protein structure prediction.
Article in Computational and structural biotechnology journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 40 citations in OpenAlex.
- Computer-aided structural modeling and drug discovery for G-protein-coupled receptors in the age of artificial intelligence.Current opinion in structural biology · 2026Review
- Establishing FDA-approved oncology drugs as GPR176 inhibitor through homology modelling, molecular docking, MMGBSA, DFT, and molecular dynamics simulation.Scientific reports · 2026Article
- Accelerating GPCR drug discovery through computation and experiment integrated with direct detection of ligand binding events.npj drug discovery · 2026Review
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- Generalizable compound protein interaction prediction with a model incorporating protein structure aware and compound property aware language model representations.Communications chemistry · 2025Article
- Deep learning in GPCR drug discovery: benchmarking the path to accurate peptide binding.Briefings in bioinformatics · 2025Article
- Ligand-Induced Biased Activation of GPCRs: Recent Advances and New Directions from In Silico Approaches.Molecules (Basel, Switzerland) · 2025Review
- Evolutionary scenarios for the specific recognition of nonhomologous endogenous peptides by G protein-coupled receptor paralogs.The Journal of biological chemistry · 2025Article
- AI meets physics in computational structure-based drug discovery for GPCRs.npj drug discovery · 2025Review
- GprC of the nematode-trapping fungus Arthrobotrys flagrans activates mitochondria and reprograms fungal cells for nematode hunting.Nature microbiology · 2024Article
- Comparative Structure-Based Virtual Screening Utilizing Optimized AlphaFold Model Identifies Selective HDAC11 Inhibitor.International journal of molecular sciences · 2024Article
- Predictive Modeling of Proteins Encoded by a Plant Virus Sheds a New Light on Their Structure and Inherent Multifunctionality.Biomolecules · 2024Article
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
While deep learning (DL) has brought a revolution in the protein structure prediction field, still an important question remains how the revolution can be transferred to advances in structure-based drug discovery. Because the lessons from the recent GPCR dock challenge were inconclusive primarily due to the size of the dataset, in this work we further elaborated on 70 diverse GPCR complexes bound to either small molecules or peptides to investigate the best-practice modeling and docking strategies for GPCR drug discovery. From our quantitative analysis, it is shown that substantial improvements in docking and virtual screening have been possible by the advance in DL-based protein structure predictions with respect to the expected results from the combination of best pre-DL tools. The success rate of docking on DL-based model structures approaches that of cross-docking on experimental structures, showing over 30% improvement from the best pre-DL protocols. This amount of performance could be achieved only when two modeling points were considered properly: 1) correct functional-state modeling of receptors and 2) receptor-flexible docking. Best-practice modeling strategies and the model confidence estimation metric suggested in this work may serve as a guideline for future computer-aided GPCR drug discovery scenarios.
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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.