The SPP 2363, entitled "Use and Development of Machine Learning for Molecular Applications - Molecular Machine Learning", is an interdisciplinary and collaborative Priority Program funded by the DFG. The goals of the program include the development of new molecular representations, the establishment of machine learning as a tool for theoretical and organic chemistry, and the application of machine learning for medicinal chemistry and drug design. These objectives will be based on the generation and evaluation of high quality data sets and the utilization and development of modern and explainable machine learning algorithms.
SPP 2363 - Molecular Machine Learning
This is the Github of the SPP 2363, entitled "Use and Development of Machine Learning for Molecular Applications - Molecular Machine Learning".
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Showing 10 of 31 repositories
- PhD_Tutorials Public
A repository for tutorials and notebooks on various topics of molecular machine learning
- 06_xAI_in_chemistry Public Forked from aimat-lab/xai_chem_review
Companion Jupyter Notebook Tutorials for XAI in Chemistry
- LFaB-for-QC Public Forked from vivinvinod/LFaB-for-QC
Code repo for study involving use of low fidelity informed uncertainty as an AL strategy for training data sampling.
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- EnT_Substrate_Mapping Public Forked from le-schlo/EnT_Substrate_Mapping
Accelerated Discovery in Dearomative Energy Transfer Catalysis through Data-Guided Reaction Screening
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