Fig. 1: Overall workflow combining HTE and bayesian deep learning to estimate reaction feasibility and robustness against environmental factors. | Nature Communications

Fig. 1: Overall workflow combining HTE and bayesian deep learning to estimate reaction feasibility and robustness against environmental factors.

From: Towards global reaction feasibility and robustness prediction with high throughput data and bayesian deep learning

Fig. 1

Wetlab data is collected using automated HTE, followed by probabilistic modeling using Bayesian neural networks. The uncertainty is disentangled into epistemic uncertainty and aleatoric uncertainty. Epistemic uncertainty originates from insufficient data and is used for further design of experiments (DoE). Aleatoric uncertainty is linked to the intrinsic noise of experimentation and is demonstrated to be an indicator of reaction robustness. We also found extensive HTE exploration enhances the quality of uncertainty estimation.

Back to article page