Virtual screening of GPCRs: an in silico chemogenomics approach.
about
Genome-scale screening of drug-target associations relevant to Ki using a chemogenomics approachMarine natural products as breast cancer resistance protein inhibitorsUnderstanding functional residues of the cannabinoid CB1.Target prediction utilising negative bioactivity data covering large chemical spaceMachine learning assisted design of highly active peptides for drug discoveryMachine learning for in silico virtual screening and chemical genomics: new strategiesComputational chemogenomics: is it more than inductive transfer?Computational models for in-vitro anti-tubercular activity of molecules based on high-throughput chemical biology screening datasets.Screening of selective histone deacetylase inhibitors by proteochemometric modeling.Application of kernel functions for accurate similarity search in large chemical databases.Learning a peptide-protein binding affinity predictor with kernel ridge regression.Improved large-scale prediction of growth inhibition patterns using the NCI60 cancer cell line panel.Platforms for the identification of GPCR targets, and of orthosteric and allosteric modulators.Support vector machines for drug discovery.Feature Selection in the Tensor Product Feature Space.Benchmarking of protein descriptor sets in proteochemometric modeling (part 2): modeling performance of 13 amino acid descriptor sets.Benchmarking of protein descriptor sets in proteochemometric modeling (part 1): comparative study of 13 amino acid descriptor sets.Cross-pharmacology analysis of G protein-coupled receptorsBeyond the hype: deep neural networks outperform established methods using a ChEMBL bioactivity benchmark set.Protein-ligand interaction prediction: an improved chemogenomics approach.Modelling ligand selectivity of serine proteases using integrative proteochemometric approaches improves model performance and allows the multi-target dependent interpretation of features.Methods for Virtual Screening of GPCR Targets: Approaches and Challenges.Prediction of chemical-protein interactions: multitarget-QSAR versus computational chemogenomic methods.Proteochemometric Modeling of the Interaction Space of Carbonic Anhydrase and its Inhibitors: An Assessment of Structure-based and Sequence-based Descriptors.Proteochemometric modeling as a tool to design selective compounds and for extrapolating to novel targetsPolypharmacology modelling using proteochemometrics (PCM): recent methodological developments, applications to target families, and future prospects
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P2860
Virtual screening of GPCRs: an in silico chemogenomics approach.
description
2008 nî lūn-bûn
@nan
2008 թուականի Սեպտեմբերին հրատարակուած գիտական յօդուած
@hyw
2008 թվականի սեպտեմբերին հրատարակված գիտական հոդված
@hy
2008年の論文
@ja
2008年論文
@yue
2008年論文
@zh-hant
2008年論文
@zh-hk
2008年論文
@zh-mo
2008年論文
@zh-tw
2008年论文
@wuu
name
Virtual screening of GPCRs: an in silico chemogenomics approach.
@ast
Virtual screening of GPCRs: an in silico chemogenomics approach.
@en
type
label
Virtual screening of GPCRs: an in silico chemogenomics approach.
@ast
Virtual screening of GPCRs: an in silico chemogenomics approach.
@en
prefLabel
Virtual screening of GPCRs: an in silico chemogenomics approach.
@ast
Virtual screening of GPCRs: an in silico chemogenomics approach.
@en
P2860
P356
P1433
P1476
Virtual screening of GPCRs: an in silico chemogenomics approach.
@en
P2093
Laurent Jacob
Véronique Stoven
P2860
P2888
P356
10.1186/1471-2105-9-363
P577
2008-09-06T00:00:00Z
P5875
P6179
1022837599