The Team
These are the Volkamer Lab members.
Group Leader
Prof. Dr. Andrea Volkamer
Group leader
Administrative Assistance
Stefanie Pilhofer
Administrative assistance
Members
Dr. Michael Backenköhler
PostDoc (UdS)
Neuro-explicit models for drug discovery
Yonghui Chen
PhD candidate (Charité)
Virtual screening and deep learning for drug discovery
Paula Linh Kramer
PhD candidate (UdS)
Neuro-explicit models for drug discovery (in co-supervision with Prof. Dr. Verena Wolf)
Floriane Odje
PhD candidate (UdS)
Development of DL models based on morphological and molecular data. BMBF-funded MORPHEUS project
Associated members
Alexander Gujarnov
PhD candidate (Bayer)
Biostatistics and in silico toxicology for development of virtual control groups at Bayer AG
Students
Armin Ariamajd
Master student, Chemistry (FU Berlin)
Modeling of target-focused dynamic pharmacophores, using molecular dynamics simulations.
Hamza Ibrahim
Master student, Bioinformatics (UdS)
Computational workflow to discover new inhibitors for ECF-T
Katharina Buchthal
Bachelor student, Bioinformatics (UdS)
Subpocket-based docking for kinases
Alumni
- Talia B. Kimber (PhD candidate)
Website
Machine learning for kinase drug discovery - Dominique Sydow (PhD candidate)
LinkedIn
Binding site comparison for off-target and polypharmacology prediction - Dr. David Schaller (Postdoc)
Free energy calculations and machine learning for kinase drug discovery with focus on mutations - Dr. Corey Taylor (Postdoc)
Machine learning and method development for kinase drug discovery - Jacqueline 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 - Dr. Jaime Rodríguez-Guerra (Postdoc [NOW - Software Engineer at Quansite])
Scalable alchemical free energy calculations and machine learning for kinase drug discovery - Andrea Morger (PhD candidate)
In silico toxicity prediction and application to external data - 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)