Machine Learning ⊗ Science
A blog about machine learning for the physical sciences, with a focus on molecular dynamics simulations.
Interests
- Deep Learning
- Drug Discovery
- Physical Chemistry
- Methods Development for Molecular Dynamics
Education
Ph.D. - Machine Learning for Physical Chemistry, (2025, expected)
The University of Basel, Switzerland
Research Stay, (2020)
Cancer and Ageing Research Program, QUT/TRI, Australia
B. Adv. Sci. (Hons. Class I) - Chemistry, (2018)
The University of Queensland, Australia
Selected Publications
(2024).
Kernel-based Minimal Distributed Charges: A Conformationally Dependent ESP-Model for Molecular Simulations.
J. Chem. Theory Comput..
(2022).
Molecular Dynamics with Conformationally Dependent, Distributed Charges.
J. Chem. Theory Comput..
(2021).
Impact of the Characteristics of Quantum Chemical Databases on Machine Learning Prediction of Tautomerization Energies.
J. Chem. Theory Comput.
(2021).
Transfer Learning to CCSD(T): Accurate Anharmonic Frequencies from Machine Learning Models.
J. Chem. Theory Comput.
(2020).
Assessing Molecular Docking Tools to Guide Targeted Drug Discovery of CD38 Inhibitors.
Int. J. Mol. Sci..
(2020).
GlycoTorch Vina: Docking Designed and Tested for Glycosaminoglycans.
J. Chem. Inf. Model.
Contact
- ericdavid.boittier@unibas.ch
- Klingelbergstr 88, Basel, Basel-Stadt 4056
- DMs Open