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Very well deserved!

1/ Who doesn’t love internships in proteinML research and open science? Check out the recent work by Profluent intern and Caltech PhD candidate Jason Yang .

Great read! Thanks for highlighting ALDE!

Evaluation of Machine Learning-Assisted Directed Evolution Across Diverse Combinatorial Landscapes • This study explores the impact of machine learning-assisted directed evolution (MLDE) across 16 combinatorial fitness landscapes, including proteins involved in binding and

Very useful evaluation of machine learning strategies for #proteinengineering #directedevolution by Francesca-Zhoufan Li @ ICLR'25 Yisong Yue Jason Yang Kadina Johnston and coworkers. #MLDE #machinelearning #AIforproteins

Machine learning-guided directed evolution strategies exceeded or at least matched DE performance with the advantages becoming more pronounced as landscapes had fewer active variants and more local optima. Francesca-Zhoufan Li @ ICLR'25 Yisong Yue Jason Yang Kadina Johnston Frances Arnold

CARE: a Benchmark Suite for the Classification and Retrieval of Enzymes • This paper introduces CARE, a benchmarking suite for enzyme function prediction, focusing on two primary tasks: enzyme classification by EC number (Task 1) and enzyme retrieval based on chemical reactions

I’m at NeurIPS this week presenting two of my recent projects! Enzyme function (CARE) benchmarks: Friday 11-2pm West Ballroom #5205 Caltech Conditional generation from PLMs (ProCALM): Sunday at MLSB Workshop Profluent Please come say hi!

Come visit our CARE benchmarks poster today at 11-2pm! (West Ballroom #5205)

Happy to share that our work on Active Learning-Assisted Directed Evolution is now published in Nature Communications! We show that it's an effective and broadly applicable method to accelerate protein engineering with machine learning. Paper: nature.com/articles/s4146…

I’ll be at #ICLR2025 in Singapore this week! I’ll also be presenting some new work at the GEM workshop on Sun, April 27. Please reach out if you want to link up!

Steering Generative Models with Experimental Data for Protein Fitness Optimization １．This paper introduces SGPO (Steered Generation for Protein Optimization), a principled and scalable framework that guides generative models of protein sequences using small amounts of real

Steered generation for protein optimization: On datasets with ~10^2 measurements, steering a discrete diffusion model outperforms RL on a protein language model for generating improved variants. Jason Yang Wenda Chu Frances Arnold Yisong Yue

I absolutely love this paper!! 🌟 Beautiful work by Jason Yang Yisong Yue and team to show that discrete diffusion models can be effectively guided by fitness data from low-throughput wet-lab assays! 🧪 I'm telling you guys: guided sequence generation is where it's at! 🤙

Jason Yang, Wenda Chu & team doing some awesome work in helping us better understand wetlab-in-the-loop guided diffusion for protein optimization.