  Enzymes are important proteins that catalyze chemical reactions. In recent
years, machine learning methods have emerged to predict enzyme function from
sequence; however, there are no standardized benchmarks to evaluate these
methods. We introduce CARE, a benchmark and dataset suite for the
Classification And Retrieval of Enzymes (CARE). CARE centers on two tasks: (1)
classification of a protein sequence by its enzyme commission (EC) number and
(2) retrieval of an EC number given a chemical reaction. For each task, we
design train-test splits to evaluate different kinds of out-of-distribution
generalization that are relevant to real use cases. For the classification
task, we provide baselines for state-of-the-art methods. Because the retrieval
task has not been previously formalized, we propose a method called Contrastive
Reaction-EnzymE Pretraining (CREEP) as one of the first baselines for this
task. CARE is available at https://github.com/jsunn-y/CARE/.
