The Team

These are the Volkamer Lab members.

Group Leader

Members

Students

  • Jacquekine Krohn (Master student)
    Data splitting schemes to evaluate the performance of ML-based molecular activity prediction
  • Mareike Leja (Master student)
    Virtual Screening Pipeline for Drug-Like PPIP5K2 Inhibitors
  • Sonja Rothkugel (Master student)
    Custom-KinFragLib - Exploring filter strategies to reduce the library size
  • Julian Pipart (Bachelor student)
    Structural Alignment tool for Python

Alumni

  • Dr. Jaime Rodríguez-Guerra (Postdoc [NOW - Software Engineer at Quansite])
    Scalable alchemical free energy calculations and machine learning for kinase drug discovery
  • Lisa Chiara Gosch (Dr. med. candidate)
    Computational and experimental testing of novel EGFR and HDAC inhibitors (co-supervised with Prof. Höpfner)
  • Franziska Fritz (Master student project work)
    PLIPify: Protein-Ligand Interaction Frequencies across Multiple Structures.
  • Michele Wichmann (Master student)
    Dynamic pharmacophore modeling from apo structures
  • Allen Dumler (Bachelor student)
    Standardization pipelines in computer-aided drug design
  • Sakshi Misra (Intern)
    Advancing TeachOpenCADD with machine learning notebooks
  • Paula Schmiel (Master student and short term scientist)
    Subpocket-based fragmentation and recombination of kinase inhibitors
  • Robert Strothmann (Bachelor student)
    Automatic generation of T²F-Flex pharmacophores based on MD simulations
  • Henry Webel (Scientist)
    Predicting cytotoxicity using deep neural networks
  • Boran Adas (Master thesis and GoogleSummerOfCode)
    Robust machine learning pipeline for classification problems in cheminformatics
  • Dr. Jérémie Mortier (Scientist)
    Truly target-focused pharmacophore modeling (T²F-Pharm)
  • Shalini Muralikumar (Intern)
    In silico investigation of protein-protein interactions during sumoylation of Smyd1
  • Pratik Dhakal (Student assistant and Master thesis)
    Truly target-focused (dynamic) pharmacophore modeling (T²F-Pharm and T²F-Flex)
  • Eva Aßmann (Bachelor thesis)
    Predicting kinase similarity using a novel fingerprint-based binding site comparison method
  • Jacob Gora (Student assistant and Master thesis with Novartis)
    Machine learning for kinase activity prediction & Active learning for compound optimization
  • Maximilian Driller (Master student project work)
    Computer-aided drug design - an interactive Python pipeline (TeachOpenCADD)
  • Angelika Szengel (Master student project work)
    Structure-based computational target prediction (review)