Chemical and biological data abound – our mission is to make sense of it.
We do so by employing cheminformatics, chemogenomics and related techniques in order to rationalize why a ligand binds to a protein target; why particular chemical features lead to toxicity; and why one compound causes a biological phenotype and another one does not. Given that current bioactivity databases contain millions of molecules; our conviction is that we should learn from the past chemistry that has been made, in order to design better molecules in the future.
One example application is shown below [Young et al.], illustrating how phenotypes (in this case from high-content screening) can be rationalized using the chemical structure of ligands, and predicted targets, based on 600,000+ bioactivity data points available in current databases.
We also work in the area of ligand toxicity prediction – recent examples include the prediction of compounds, which inhibit the hERG channel [Doddareddy et al.], a potassium channel implicated with ‘sudden death syndrome’ caused even by approved drugs. Common to all of our studies is the analysis of chemical structures, with the goal to design better bioactive matter in the future.
Selected Publications
A novel chemogenomics analysis of G protein coupled receptors (GPCRs) and their ligands: a potential strategy for receptor de-orphanization. Eelke van der Horst et al. BMC Bioinformatics (2010) (11) 316
Prospective Validation of a Comprehensive In silico hERG model and its Applications to Commercial Compound and Drug Databases. Munikumar R. Doddareddy et al.,ChemMedChem (2010) (5) 716 - 729
Integrating high-content screening and ligand-target prediction to identify mechanism of action. Daniel Young et al., Nature Chem. Biol. (2008) (4) 59 - 68
Multi-parameter Phenotypic Profiling: Characterization of Small-molecule Compounds Based on Their Effects on Cells. Yan Feng et al., Nature Rev. Drug. Discov. (2009) (8) 567 – 578
Recent publications from the department database
Experimental validation of in silico target predictions on synergistic protein targets
Cortes-Ciriano I, Koutsoukas A, Abian O, Glen RC, Velazquez-Campoy A, Bender A
MedChemComm 2013, 4, 278—288. DOI:
Significantly improved HIV inhibitor efficacy prediction employing proteochemometric models generated from antivirogram data.
van Westen GJ, Hendriks A, Wegner JK, Ijzerman AP, van Vlijmen HW, Bender A
2013, e1002899 —. DOI: 10.1371/journal.pcbi.1002899
Chemogenomics Approaches to Rationalizing the Mode-of-Action of Traditional Chinese and Ayurvedic Medicines.
Mohd Fauzi F, Koutsoukas A, Lowe R, Joshi K, Fan TP, Glen RC, Bender A
2013, —. DOI: 10.1021/ci3005513
A-ring dihalogenation increases the cellular activity of combretastatin-templated tetrazoles
Beale TM, Allwood DM, Bender A, Bond PJ, Brenton JD, Charnock-Jones DS, Ley SV, Myers RM, Shearman JW, Temple J, Unger J, Watts CA, Xian J
ACS Medicinal Chemistry Letters 2012, 177—181. DOI:
Using multiobjective optimization and energy minimization to design an isoform-selective ligand of the 14-3-3 protein
Sanchez-Faddeev H, Emmerich MTM, Verbeek FJ, Henry AH, Grimshaw S, Spaink HP, Van Vlijmen HW, Bender A
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2012, 7610 LNCS, 12—24. DOI: