Zelda Mariet, Ghassen Jerfel, Zi Wang, Christof Angermüller, David Belanger, Suhani Vora, Maxwell Bileschi, Lucy Colwell, D Sculley, Dustin Tran, Jasper Snoek
Machine Learning for Molecules Workshop (NeurIPS 2020)
Publication year: 2020

Machine learning applications to molecule and protein design require models that provide meaningful uncertainty estimates. For example, Bayesian optimization for biomolecules searches through the space of viable designs, trading off the exploration of uncertain regions with the exploitation of high-value areas. We introduce protein optimization datasets as a benchmarking environment for ML uncertainty on real-world distribution shifts; investigate scalable models robust to the distribution shift inherent to large-batch, multi-round BO over protein space; and show that intra-ensemble diversification improves calibration on multi-round regression tasks, allowing for more principled biological compound design.