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Abstract: In computational drug discovery we often rely on surrogate models for molecular design and optimization. The estimates of the epistemic uncertainty of those surrogate models can be useful signals to enable efficient exploration in the molecular space. Traditional methods such as Bayesian optimization use Bayesian models like Gaussian Processes (GPs) as surrogates. Gaussian processes provide well calibrated uncertainty estimates, but do not scale trivially to large datasets and require hand-crafted kernels to work with structured data like strings (SMILES, peptides) and graphs...

Speaker: Moksh Jain - https://mj10.github.io/ 

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