Esther Heid
Ass. Prof. Dr. rer. nat.Member of the Young Academy since 2026
- Professorin für Machine Learning für nachhaltige Chemie an der TU Wien
Orcid-ID:
0000-0002-8404-6596
Research Areas:
- Chemistry
- Computational chemistry
- Machine learning
- Artificial intelligence
- Theoretical chemistry
Profile:
Publications:
Selected Memberships:
- Chemisch-physikalische Gesellschaft Wien
Selected Prizes:
- ERC Starting Grant
- FWF START Grant
- Award of Excellence des BMBWF
- Karl Schlögl-Preis der ÖAW
- Loschmidt-Preis der Chemisch-physikalischen Gesellschaft Wien
Selected Publications:
- Chemprop: A Machine Learning Package for Chemical Property Prediction. E. Heid, K. P. Greenman, Y. Chung, S.-C. Li, D. E. Graff, F. H. Vermeire, H. Wu, W. H. Green, C. J. McGill. J. Chem. Inf. Model., 2024, 64, 9-17.
- Machine Learning of Reaction Properties via Learned Representations of the Condensed Graph of Reaction. E. Heid, W. H. Green. J. Chem. Inf. Model., 2022, 62, 2101–2110.
- Characterizing Uncertainty in Machine Learning for Chemistry. E. Heid, C. J. McGill, F. Vermeire, W. H. Green. J. Chem. Inf. Model., 2023, 63, 4012–4029.
- EnzymeMap: Curation, validation and data-driven prediction of enzymatic reactions. E. Heid, D. Probst, W. H. Green and G. K. H. Madsen. Chem. Sci., 2023, 14, 14229.
- ChemTorch: A Deep Learning Framework for Benchmarking and Developing Chemical Reaction Property Prediction Models. J. De Landsheere, A. Zamyatin, J. Karwounopoulos, E. Heid. J. Chem. Inf. Model., 2025, 66, 2434-2442.