Publications

Supporting Information of publications.

Kinase profiling studies  
└── Profiling prediction of kinase inhibitors
└── Kinome-wide profiling prediction of small molecules

Compound optimization tools
└── Coupling_MMPs_with_ML

Featurizations of molecules - employing natural language processing techniques
└── Mol2vec_Learning_vector_representations_of_molecular_substructures

Structure-based modelling
└── Modelling_DFG-out_structures

These projects were supported by BioMed X Innovation Center, Heidelberg

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• Kinome-wide profiling prediction of small molecules
  Sorgenfrei F.A., Fulle, S., Merget, B., ChemMedChem, 2018, 13, 495-499. Link.

• Profiling prediction of kinase inhibitors
  Merget, B., Turk, S., Eid, S., Rippmann, F., Fulle, S., J. Med. Chem., 2017, 60, 474−485. Link.

• Coupling matched molecular pairs with machine learning for virtual compound optimization
  Turk, S., Merget, B., Rippmann, F, Fulle, S., J. Chem. Inf. Model., 2017, 57, 3079-3085. Link.

• Mol2vec: Unsupervised machine learning approach with chemical intuition
  Jaeger, S., Fulle, S., Turk, S., J. Chem. Inf. Model., 2018, 58, 27-35. Link.

• Modelling conformational flexibility of kinases in inactive states
  Schwarz, D., Merget, B., Deane, C., Fulle, S., submitted.