Christoph Bauer

AstraZeneca (Australia) — Australia

Computer Science · Physical Sciences

17h-index3.1kcitations59works1.32yr avg
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Research Topics

Computational Drug Discovery Methods(17), Machine Learning in Materials Science(15), X-ray Spectroscopy and Fluorescence Analysis(10), Ion-surface interactions and analysis(10), Advanced Chemical Physics Studies(9)

Publications59 total

Prediction of Retention Time in Larger Antisense Oligonucleotide Datasets using Machine Learning
arXiv (Cornell University)·2025Open Access
Precedent Finder: Locating Pareto-Optimal Reactions
Journal of Chemical Information and Modeling·2025· 1 cited
Prediction of retention time in larger antisense oligonucleotide datasets using machine learning
Machine Learning with Applications·2025Open Access
Precedent Finder – Locating Pareto-Optimal Reactions
2025· 1 cited
Finding Relevant Retrosynthetic Disconnections for Stereocontrolled Reactions
Journal of Chemical Information and Modeling·2024· 5 cited
Finding relevant retrosynthetic disconnections for stereocontrolled reactions
2024· 1 citedOpen Access
An open-source framework for fast-yet-accurate calculation of quantum mechanical features
Physical Chemistry Chemical Physics·2022· 6 cited
Publisher Correction: Organic reactivity from mechanism to machine learning
Nature Reviews Chemistry·2021· 3 cited
Organic reactivity from mechanism to machine learning
Nature Reviews Chemistry·2021· 173 cited
Development and Implementation of a Neural Network-Based Abnormal State Prediction System for a Piston Pump
Lecture notes in computer science·2021
Shape Similarity by Fractal Dimensionality: An Application in the de novo Design of (−)‐Englerin A Mimetics
ChemMedChem·2020· 8 cited
CCDC 1898014: Experimental Crystal Structure Determination
The Cambridge Structural Database·2020Open Access
ALADDIN: Docking Approach Augmented by Machine Learning for Protein Structure Selection Yields Superior Virtual Screening Performance
Molecular Informatics·2019· 10 citedOpen Access
Skin Doctor: Machine Learning Models for Skin Sensitization Prediction that Provide Estimates and Indicators of Prediction Reliability
International Journal of Molecular Sciences·2019· 25 citedOpen Access
Machine learning models for hydrogen bond donor and acceptor strengths using large and diverse training data generated by first-principles interaction free energies
Journal of Cheminformatics·2019· 33 citedOpen Access
Skin Doctor: Machine Learning Models for Skin Sensitization Prediction that Provide Estimates and Indicators of Prediction Reliability
2019Open Access
How to Model Inter- and Intramolecular Hydrogen Bond Strengths with Quantum Chemistry
Journal of Chemical Information and Modeling·2019· 11 cited
MOESM7 of Machine learning models for hydrogen bond donor and acceptor strengths using large and diverse training data generated by first-principles interaction free energies
Figshare·2019Open Access
MOESM6 of Machine learning models for hydrogen bond donor and acceptor strengths using large and diverse training data generated by first-principles interaction free energies
Figshare·2019Open Access
MOESM2 of Machine learning models for hydrogen bond donor and acceptor strengths using large and diverse training data generated by first-principles interaction free energies
Figshare·2019Open Access
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Frequent Co-authors

Wolfram W. Rudolph(11), Stefan Grimme(10), P. Gippner(7), Per‐Ola Norrby(6), Gisbert Schneider(6), D. Grambole(5), C. Heiser(5), R. Mann(5), H.-J. Thomas(4), Elmar W. Lang(4), Samuel Genheden(4), K. Brankoff(3), Conrad Stork(3), Johannes Kirchmair(3), K. Hohmuth(3), Andreas Hansen(2), Kjell Jorner(2), Anna Tomberg(2), Christian Sköld(2), Vilhjálmur Ásgeirsson(2)