Additionally, Rabbani et al. and An et al. demonstrated a combination of machine learning and directed evolution. Rabbani et al. showed, that an ensemble of different models can be used to further optimize sequences that were optimized via directed evolution for mycotoxin by 5 to 40%. In contrast, An et al. showed a combination of directed evolution and machine learning to optimize nanosensors with improved sensitivity of serotonin over dopamine. Therefore, computational tools are very effective in navigating the huge chemical design space and proposing novel chemistries. In a certain way they are also linked to the high throughput capability of the experimental approaches because they are most effective with huge training data set.