We have developed fine-tuned algorithms involving convolutional neural networks, support vector machines, and regression fitting to predict hotspots and substitutions in enzymes. We build structure-activity relationship models on a daily basis to predict the kinetic properties of enzymes and their variants to differentiate enzymes with low and high activity. All said and done it is to get a focused library of enzymes with minimal false positives.