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Applications of proteochemometrics - from species extrapolation to cell line sensitivity modelling.

, , , , , and . BMC Bioinform., 16 (S-3): A4 (2015)

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Proteochemometric modeling in a Bayesian framework., , , , , and . J. Cheminformatics, 6 (1): 35 (2014)Selecting an Optimal Number of Binding Site Waters To Improve Virtual Screening Enrichments Against the Adenosine A2A Receptor., , , , and . J. Chem. Inf. Model., 54 (6): 1737-1746 (2014)Accurate Prediction of GPCR Ligand Binding Affinity with Free Energy Perturbation., , , , , , , and . J. Chem. Inf. Model., 60 (11): 5563-5579 (2020)Beyond the hype: deep neural networks outperform established methods using a ChEMBL bioactivity benchmark set., , , , , , , and . J. Cheminformatics, 9 (1): 45 (2017)Multitask machine learning models for predicting lipophilicity (logP) in the SAMPL7 challenge., and . J. Comput. Aided Mol. Des., 35 (8): 901-909 (2021)Interacting with GPCRs: Using Interaction Fingerprints for Virtual Screening., , , , and . Journal of Chemical Information and Modeling, 56 (10): 2053-2060 (2016)Drug Discovery Maps, a Machine Learning Model That Visualizes and Predicts Kinome-Inhibitor Interaction Landscapes., , , , , , , and . J. Chem. Inf. Model., 59 (3): 1221-1229 (2019)In search of novel ligands using a structure-based approach: a case study on the adenosine A2A receptor., , , , , , and . J. Comput. Aided Mol. Des., 30 (10): 863-874 (2016)Relative Binding Free Energy Calculations Applied to Protein Homology Models., , , , , , and . J. Chem. Inf. Model., 56 (12): 2388-2400 (2016)Applications of proteochemometrics - from species extrapolation to cell line sensitivity modelling., , , , , and . BMC Bioinform., 16 (S-3): A4 (2015)