Modelling ligand selectivity of serine proteases using integrative proteochemometric approaches improves model performance and allows the multi-target dependent interpretation of features.
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Drug Design for CNS Diseases: Polypharmacological Profiling of Compounds Using Cheminformatic, 3D-QSAR and Virtual Screening MethodologiesBioalerts: a python library for the derivation of structural alerts from bioactivity and toxicity data setsprotr/ProtrWeb: R package and web server for generating various numerical representation schemes of protein sequences.In silico studies in drug research against neurodegenerative diseases.The recent progress in proteochemometric modelling: focusing on target descriptors, cross-term descriptors and application scope.Kinome-Wide Profiling Prediction of Small Molecules.
P2860
Modelling ligand selectivity of serine proteases using integrative proteochemometric approaches improves model performance and allows the multi-target dependent interpretation of features.
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name
Modelling ligand selectivity o ...... nt interpretation of features.
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Modelling ligand selectivity o ...... nt interpretation of features.
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type
label
Modelling ligand selectivity o ...... nt interpretation of features.
@en
Modelling ligand selectivity o ...... nt interpretation of features.
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Modelling ligand selectivity o ...... nt interpretation of features.
@en
Modelling ligand selectivity o ...... nt interpretation of features.
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P2093
P2860
P50
P356
P1433
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Modelling ligand selectivity o ...... nt interpretation of features.
@en
P2093
Andreas Bender
Qurrat U Ain
Thérèse Malliavin
P2860
P304
P356
10.1039/C4IB00175C
P577
2014-11-01T00:00:00Z