| scholarly article | Q13442814 |
| P818 | arXiv ID | 0801.4301 |
| P8978 | DBLP publication ID | journals/bmcbi/JacobHSV08 |
| P6179 | Dimensions Publication ID | 1022837599 |
| P356 | DOI | 10.1186/1471-2105-9-363 |
| P932 | PMC publication ID | 2553090 |
| P698 | PubMed publication ID | 18775075 |
| P5875 | ResearchGate publication ID | 23241131 |
| P50 | author | Brice Hoffmann | Q57021498 |
| Jean-Philippe Vert | Q41044597 | ||
| P2093 | author name string | Véronique Stoven | |
| Laurent Jacob | |||
| P2860 | cites work | Molecular tinkering of G protein-coupled receptors: an evolutionary success | Q24534016 |
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| Assessing the performance of OMEGA with respect to retrieving bioactive conformations | Q78834325 | ||
| A crystal clear view of the beta2-adrenergic receptor | Q80657778 | ||
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| G-protein-coupled receptors: past, present and future. | Q35566599 | ||
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| Graph kernels for chemical informatics | Q36255619 | ||
| Targeting G protein-coupled receptor signaling in asthma | Q36530502 | ||
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| Structural models of class a G protein-coupled receptors as a tool for drug design: insights on transmembrane bundle plasticity | Q36825015 | ||
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| Similarity metrics for ligands reflecting the similarity of the target proteins | Q44373337 | ||
| The retinal conformation and its environment in rhodopsin in light of a new 2.2 A crystal structure | Q45029374 | ||
| Collaborative filtering on a family of biological targets. | Q45966036 | ||
| Coupling ligand structure to specific conformational switches in the beta2-adrenoceptor | Q46120520 | ||
| A physicogenetic method to assign ligand-binding relationships between 7TM receptors | Q46579290 | ||
| Protein homology detection using string alignment kernels | Q48028746 | ||
| A tree kernel to analyse phylogenetic profiles. | Q48625579 | ||
| Marginalized kernels for biological sequences. | Q48625590 | ||
| P275 | copyright license | Creative Commons Attribution 2.0 Generic | Q19125117 |
| P6216 | copyright status | copyrighted | Q50423863 |
| P921 | main subject | G protein-coupled receptor | Q38173 |
| in silico | Q192572 | ||
| virtual screening | Q4112105 | ||
| P304 | page(s) | 363 | |
| P577 | publication date | 2008-09-06 | |
| P1433 | published in | BMC Bioinformatics | Q4835939 |
| P1476 | title | Virtual screening of GPCRs: an in silico chemogenomics approach | |
| P478 | volume | 9 |
| Q33571101 | Application of kernel functions for accurate similarity search in large chemical databases. |
| Q41760025 | Benchmarking of protein descriptor sets in proteochemometric modeling (part 1): comparative study of 13 amino acid descriptor sets |
| Q39411048 | Benchmarking of protein descriptor sets in proteochemometric modeling (part 2): modeling performance of 13 amino acid descriptor sets |
| Q42695674 | Beyond the hype: deep neural networks outperform established methods using a ChEMBL bioactivity benchmark set. |
| Q38915500 | CompoundProtein Interaction Prediction Within Chemogenomics: Theoretical Concepts, Practical Usage, and Future Directions |
| Q30391368 | Computational Prediction of DrugTarget Interactions Using Chemical, Biological, and Network Features |
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| Q30414815 | Computational models for in-vitro anti-tubercular activity of molecules based on high-throughput chemical biology screening datasets |
| Q41875668 | Cross-pharmacology analysis of G protein-coupled receptors |
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| Q38258721 | Feature Selection in the Tensor Product Feature Space. |
| Q21133651 | Genome-scale screening of drug-target associations relevant to Ki using a chemogenomics approach |
| Q36379132 | Improved large-scale prediction of growth inhibition patterns using the NCI60 cancer cell line panel |
| Q34622472 | Learning a peptide-protein binding affinity predictor with kernel ridge regression |
| Q28545910 | Machine learning assisted design of highly active peptides for drug discovery |
| Q28751419 | Machine learning for in silico virtual screening and chemical genomics: new strategies |
| Q27027706 | Marine natural products as breast cancer resistance protein inhibitors |
| Q45943415 | Methods for Virtual Screening of GPCR Targets: Approaches and Challenges. |
| Q45383758 | Modelling ligand selectivity of serine proteases using integrative proteochemometric approaches improves model performance and allows the multi-target dependent interpretation of features |
| Q38028542 | Platforms for the identification of GPCR targets, and of orthosteric and allosteric modulators. |
| Q54152494 | Polypharmacology modelling using proteochemometrics (PCM): recent methodological developments, applications to target families, and future prospects |
| Q47428755 | Prediction of chemical-protein interactions: multitarget-QSAR versus computational chemogenomic methods. |
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| Q51319882 | Proteochemometric Modeling of the Interaction Space of Carbonic Anhydrase and its Inhibitors: An Assessment of Structure-based and Sequence-based Descriptors. |
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| Q30420578 | Screening of selective histone deacetylase inhibitors by proteochemometric modeling. |
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| Q38168576 | Support vector machines for drug discovery |
| Q27902317 | Target prediction utilising negative bioactivity data covering large chemical space |
| Q27687653 | Understanding functional residues of the cannabinoid CB1. |
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