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Found 3 papers, 3 papers with code
GAUCHE: A Library for Gaussian Processes in Chemistry
By defining such kernels in GAUCHE, we seek to open the door to powerful tools for uncertainty quantification and Bayesian optimisation in chemistry.
Calibration and generalizability of probabilistic models on low-data chemical datasets with DIONYSUS
Deep learning models that leverage large datasets are often the state of the art for modelling molecular properties.
SELFIES and the future of molecular string representations
We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science.
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