Conrad Stork

BASF (Germany) — Germany

Computer Science · Physical Sciences

14h-index796citations34works8.42yr avg
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Research Topics

Computational Drug Discovery Methods(23), Contact Dermatitis and Allergies(7), Pharmacogenetics and Drug Metabolism(6), Metabolomics and Mass Spectrometry Studies(6), Machine Learning in Materials Science(6)

Publications34 total

AIMNet2‐NSE: A Transferable Reactive Neural Network Potential for Open‐Shell Chemistry
Angewandte Chemie International Edition·2025· 1 citedOpen Access
AIMNet2‐NSE: A Transferable Reactive Neural Network Potential for Open‐Shell Chemistry
Angewandte Chemie·2025Open Access
P03-10 Leveraging explainable AI (XAI) for prediction analysis
Toxicology Letters·2025
AIMNet2-NSE: A Transferable Reactive Neural Network Potential for Open-Shell Chemistry
2025· 2 citedOpen Access
MolPipeline: A Python Package for Processing Molecules with RDKit in Scikit-learn
Journal of Chemical Information and Modeling·2024· 31 cited
MolPipeline : A python package for processing molecules with RDKit in scikit-learn
2024· 6 citedOpen Access
Tackling assay interference associated with small molecules
Nature Reviews Chemistry·2024· 19 cited
Computational prediction of the metabolites of agrochemicals formed in rats
The Science of The Total Environment·2023· 6 citedOpen Access
Computational Applications in Secondary Metabolite Discovery (CAiSMD): an online workshop
Journal of Cheminformatics·2021· 6 citedOpen Access
CYPlebrity: Machine learning models for the prediction of inhibitors of cytochrome P450 enzymes
Bioorganic & Medicinal Chemistry·2021· 51 citedOpen Access
Predicting the Skin Sensitization Potential of Small Molecules with Machine Learning Models Trained on Biologically Meaningful Descriptors
Pharmaceuticals·2021· 16 citedOpen Access
Computational prediction of frequent hitters in target-based and cell-based assays
Artificial Intelligence in the Life Sciences·2021· 13 citedOpen Access
CYPstrate: A Set of Machine Learning Models for the Accurate Classification of Cytochrome P450 Enzyme Substrates and Non-Substrates
Molecules·2021· 22 citedOpen Access
BonMOLière: Small-Sized Libraries of Readily Purchasable Compounds, Optimized to Produce Genuine Hits in Biological Screens across the Protein Space
International Journal of Molecular Sciences·2021· 4 citedOpen Access
BonMOLière: Small-Sized Libraries of Readily Purchasable Compounds, Optimized to Produce Genuine Hits in Biological Screens across the Protein Space
Zenodo (CERN European Organization for Nuclear Research)·2021Open Access
BonMOLière: Small-Sized Libraries of Readily Purchasable Compounds, Optimized to Produce Genuine Hits in Biological Screens across the Protein Space
Zenodo (CERN European Organization for Nuclear Research)·2021Open Access
Predicting the skin sensitization potential of small molecules with machine learning models trained on biologically meaningful descriptors
Zenodo (CERN European Organization for Nuclear Research)·2021Open Access
Predicting the skin sensitization potential of small molecules with machine learning models trained on biologically meaningful descriptors
Zenodo (CERN European Organization for Nuclear Research)·2021Open Access
Predicting the skin sensitization potential of small molecules with machine learning models trained on biologically meaningful descriptors
Zenodo (CERN European Organization for Nuclear Research)·2021Open Access
Skin Doctor CP: Conformal Prediction of the Skin Sensitization Potential of Small Organic Molecules
Chemical Research in Toxicology·2020· 23 citedOpen Access
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Frequent Co-authors

Johannes Kirchmair(25), Neann Mathai(9), Christina de Bruyn Kops(8), Martin Šícho(7), Anke Wilm(7), Jochen Kühnl(7), Steffen Hirte(6), Ulf Norinder(5), Daniel Svozil(4), Marina García de Lomana(4), Ya Chen(3), Jochen Sieg(3), Christian Feldmann(3), Jennifer Hemmerich(3), Frederik Sandfort(3), Miriam Mathea(3), Christoph Bauer(3), Alejandra Escribano(3), Torben Steenbock(3), Carmen Herrmann(3)