Andrea Volkamer

Professor

Saarland University

Germany

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Professor Andrea Volkamer is a faculty member at Universität des Saarlandes, Germany. Her research focuses on kinase drug discovery, molecular property prediction, and computational toxicology, as evident from her recent publications. She has explored various strategies in chemical education and chemical representation learning, contributing significantly to the field of digital chemistry.

Articles (15)

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2024

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2024

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2024

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2024

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2024

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2023

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2022

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2022

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2022

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2022

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2022

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2022

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2022

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2021

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2021

Collaborators (7)

Albert Kooistra

Associate Professor

University of Copenhagen

DENMARK

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Mostafa M. Hamed

Helmholtz Institute for Pharmaceutical Research Saarland

GERMANY

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Timo Gehring

Professor of Bio and Environmental Process Engineering

Hochschule für Technik und Wirtschaft

GERMANY

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Anna Katharina Herta Hirsch

Full professor (W3)

Saarland University

GERMANY

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Kjell Jorner

Assistant Professor of Digital Chemistry

ETH Zürich

SWITZERLAND

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Sebastian Dunst

Federal Institute for Risk Assessment

GERMANY

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Nikita Janakarajan

ETH Zurich

SWITZERLAND

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Andrea Volkamer

Research Interests

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Articles (15)

Hit optimization by dynamic combinatorial chemistry on Streptococcus pneumoniae energy-coupling factor transporter ECF-PanT

Chemical education in digital chemistry

Benchmarking Cross-Docking Strategies in Kinase Drug Discovery

Transformers for Molecular Property Prediction: Lessons Learned from the Past Five Years

Guided Docking as a Data Generation Approach Facilitates Structure-Based Machine Learning on Kinases

Chemical representation learning for toxicity prediction

KiSSim: Predicting Off-Targets from Structural Similarities in the Kinome

OpenCADD-KLIFS: A Python package to fetch kinase data from the KLIFS database

Consideration of predicted small-molecule metabolites in computational toxicology

Kinase similarity assessment pipeline for off-target prediction [v1.0]

Quantitative high-throughput phenotypic screening for environmental estrogens using the E-Morph Screening Assay in combination with in silico predictions.

Studying and mitigating the effects of data drifts on ML model performance at the example of chemical toxicity data.

TeachOpenCADD 2022: open source and FAIR Python pipelines to assist in structural bioinformatics and cheminformatics research.

Assessing the calibration in toxicological in vitro models with conformal prediction

Garcinol from Garcinia indica inhibits HIV‐1 reverse transcriptase‐associated ribonuclease H

Collaborators (7)

Albert Kooistra

Mostafa M. Hamed

Timo Gehring

Anna Katharina Herta Hirsch

Kjell Jorner

Sebastian Dunst

Nikita Janakarajan

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