Comprehensive and Automated Linear Interaction Energy Based Binding-Affinity Prediction for Multifarious Cytochrome P450 Aromatase Inhibitors

Marc van Dijk, Antonius M Ter Laak, Jörg D Wichard, Luigi Capoferri, Nico P E Vermeulen, Daan P Geerke

Research output: Contribution to JournalArticleAcademicpeer-review


Cytochrome P450 aromatase (CYP19A1) plays a key role in the development of estrogen dependent breast cancer, and aromatase inhibitors have been at the front line of treatment for the past three decades. The development of potent, selective and safer inhibitors is ongoing with in silico screening methods playing a more prominent role in the search for promising lead compounds in bioactivity-relevant chemical space. Here we present a set of comprehensive binding affinity prediction models for CYP19A1 using our automated Linear Interaction Energy (LIE) based workflow on a set of 132 putative and structurally diverse aromatase inhibitors obtained from a typical industrial screening study. We extended the workflow with machine learning methods to automatically cluster training and test compounds in order to maximize the number of explained compounds in one or more predictive LIE models. The method uses protein-ligand interaction profiles obtained from Molecular Dynamics (MD) trajectories to help model search and define the applicability domain of the resolved models. Our method was successful in accounting for 86% of the data set in 3 robust models that show high correlation between calculated and observed values for ligand-binding free energies (RMSE < 2.5 kJ mol(-1)), with good cross-validation statistics.

Original languageEnglish
Pages (from-to)2294-2308
Number of pages15
JournalJournal of Chemical Information and Modeling
Issue number9
Publication statusPublished - 25 Sept 2017


We gratefully acknowledge Thomas Steger-Hartmann and Nikolaus Heinrich for their help in supplying us with experimental data from Bayer AG, and Martin Stroet and Alan Mark for support in ATB topology file generation. This work was supported by the Innovative Medicines Initiative Joint Undertaking under grant agreement no. 115002 (eTOX), resources of which are composed of financial contribution from the European Union Seventh Framework Programme (FP7/ 20072013) and EFPIA companies in kind contribution, and by The Netherlands Organization for Scientific Research (NWO, VIDI grant 723.012.105).

FundersFunder number
Netherlands Organization for Scientific Research
Seventh Framework Programme115002
European Federation of Pharmaceutical Industries and Associations
Nederlandse Organisatie voor Wetenschappelijk Onderzoek723.012.105
Seventh Framework Programme
Innovative Medicines Initiative


    • Aromatase
    • Aromatase Inhibitors
    • Automation
    • Computational Biology
    • Journal Article
    • Ligands
    • Linear Models
    • Molecular Dynamics Simulation
    • Protein Binding
    • Protein Conformation
    • Thermodynamics


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