Xiaozhou Luo

This study benchmarks machine learning models for enzyme engineering using a curated dataset, finding zero-shot predictors are most generalizable but still modestly correlated with activity, with consensus models improving precision.

OpenKinetics Predictor unifies thirteen enzyme kinetic predictors behind one interface and API; on E. coli iML1515, eight kcat predictors correlate pairwise at r 0.08 to 0.67.

This paper introduces a framework combining a protein GPT model with a custom discriminator to prioritize diverse and functional protein sequences for de novo design.

ThermoFinder is a sequence-based framework for predicting thermophilic proteins that outperforms existing tools on benchmark datasets and shows strong performance on newly constructed datasets.

This paper combines microbial and chemical synthesis to efficiently produce plitidepsin, achieving high yields and new analogues with anticancer and antiviral activities.