Enzyme Discovery Using Machine Learning

Identifying enzymes for specific chemical transformations remains a key challenge at the intersection of machine learning and biochemistry. We present a modular framework that integrates over 20 open-source tools to support ML-driven enzyme discovery from large sequence databases. As part of this, we developed a machine learning model to map between chemical reaction representations and the protein sequence space, enabling large-scale retrieval of candidate enzymes for a target transformation. We experimentally verify the predictions and identify multiple enzymes capable of degrading synthetic pollutants that are dissimilar to the training set, but enzyme discovery is far from solved. I’ll conclude by highlighting key challenges and opportunities, such as improved molecular encodings and model architectures for biochemical data.