The Team
These are the Volkamer Lab members.
Group Leader
Prof. Dr. Andrea Volkamer
Group leader
Administrative Assistance
Stefanie Wessinger
Administrative assistance
Members
Dr. Michael Backenköhler
PostDoc (UdS)
Neuro-explicit models for drug discovery
Dr. Raquel López-Ríos de Castro
Einstein BIH PostDoc (Charité)
Machine learning for kinase drug discovery
Dr. Guillermo Pérez Hernández
PostDoc (UdS)
Molecular Dynamics, Kinase Drug Discovery and Software Development
Antoine Lacour
PostDoc
DockM8 Development, Virtual Screening for NEDD, and Machine Learning for Drug Discovery
Yonghui Chen
PhD candidate (FU Berlin)
Virtual screening and deep learning for drug discovery
Paula Linh Kramer
PhD candidate (UdS)
Deep learning for novel kinase inhibitors
Floriane Odje
PhD candidate (UdS)
Development of DL models based on morphological and molecular data. BMBF-funded MORPHEUS project
Afnan Sultan
PhD candidate (UdS/BASF)
Generative AI for molecules with non-toxic properties
Yanyuan Zhang
PhD candidate (UdS)
Read-Across the Targetome
Hamza Ibrahim
PhD candidate (UdS)
Structure-based Machine Learning
Lisa-Marie Rolli
PhD candidate (UdS)
trustworthy ML methods, EU-funded RADAR project
Ana Mirandela
PhD candidate (UdS)
AI-based design of safe molecules, EU-funded RADAR project
Associated members
Joschka Groß
Associated PhD candidate (MoSi chair @ UdS, supervised by Prof. Verena Wolf)
Deep learning for kinase drug discovery
Mohammad Walid Shahrour
Associated PhD candidate (Department of Drug Design and Optimization @ HIPS )
Computer-aided drug design of novel Elastase B (LasB) inhibitors in Pseudomonas aeruginosa
Students
Fatemeh Moghaddam
Master student, Bioinformatics (UdS)
Interpretable E3-GNN affinity models
Maximilian Bähr
Bachelor student, Bioinformatics (UdS)
Active Learning
Anjali Choudhary
Master student, Bioinformatics (UdS)
Pattern identification for chemical structure and morphological fingerprint
Sakhawat Hosain Sumit
Master student, Visual computing (UdS)
TeachOpenCADD for Jupyter notebook and interpretable machine learning.
Pousali Das
Master student, Bioinformatics (UdS)
Cell painting Image Processing and Pattern Recognition (MORPHEUS)
Gilbert El Khoury
Master student, Bioinformatics (UdS)
DataM8 development - Python library for registry integration to accelerate ensemble docking workflows
Loulwah Arnaout
Master student, Bioinformatics (UdS)
Few-shot learning for toxic endpoint prediction
Aditi Lalla
Master student, Bioinformatics (UdS)
Improving molecular transformer models through data quality and chemistry awareness
Florian Sollinger
Bachelor student, Bioinformatics (UdS & supervised by Dr. Rebekka Burkholz @ CISPA)
GNNs for toxicity prediction
Prafful Sharma
Master student, Bioinformatics (UdS)
Systematic Analysis of Protein and Binding Pocket Similarity for Predicting Docking Pipeline Performance in Virtual Screening
Alumni
2026
- Zyad Barghouth (Master student, Bioinformatics (UdS))
co-supervised by Prof. Dr. Kerstin Lenhof @ UMG
Finding Optimal drug representations for Drug Sensitivity and ADMET prediction - Katharina Buchthal (Master student, Bioinformatics (UdS))
Novel kinase ligand generation using subpocket-based docking - Zenab Khan (Master student, Bioinformatics (UdS))
AI Safety Saarland Research Incubator
Interpretable ML for Toxicity Prediction Using Random Forest Graphs - Mine Caner (Master student, Bioinformatics (UdS))
AI Safety Saarland Research Incubator
Interpretable Machine Learning for Toxicity Prediction - Evgeniia (Jackie) Khodzhaeva (Master student, Bioinformatics (UdS))
Metabolic Engineering: Crafting the Perfect Cellular Factory
2025
- Ritika Bansal (Master student, Bioinformatics (UdS & Microbial Natural Products @ HIPS))
Mechanistic insights into the interaction of a novel natural product with transmembrane proteins - Lutz Herrmann (Master student, Bioinformatics (UdS))
co-supervised by Prof. Dr. Kerstin Lenhof @ UMG
Benchmarking of various neural network approaches for drug sensitivity prediction - Premnath Madanagopal (Master student, Bioinformatics (UdS))
Knowledge-guided metabolic pathway reconstruction (co-supervised by Prof. Wittmann, Systems Biotechnology) - Armin Ariamajd (Master student, Chemistry (FU Berlin))
Modeling of target-focused dynamic pharmacophores, using molecular dynamics simulations. - Heshine Gnanasekar (Master student, Bioinformatics (UdS))
Constant pH Molecular Dynamics Simulations of Abl Kinase and its inhibitors - Julien Berthet (Master student, Chemoinformatics (UniStra))
Optimizing consensus structure-based virtual screening through protein structure similarity analysis - Erika Primavera (Visiting PhD candidate (UniPg))
Molecular Dynamics and Virtual Screening for novel AKT1 inhibitors/non-conventional degraders. Co-financed by NRRP (EU) and Sibylla Biotech S.p.A.
2024
- Alexander Gujarnov (PhD candidate (Bayer))
Biostatistics and in silico toxicology for development of virtual control groups at Bayer AG
2023
- 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
2022
- 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)