3 citations · 7 across the 4 of their papers we have counts for
4 papers
Concept Bottleneck Language Models For protein design
Aya Abdelsalam Ismail, Tuomas Oikarinen, Amy Wang +8
We introduce Concept Bottleneck Protein Language Models (CB-pLM), a generative masked language model with a layer where each neuron corresponds to an interpretable concept. Our arc…
Generalists vs. Specialists: Evaluating LLMs on Highly-Constrained Biophysical Sequence Optimization Tasks
Angelica Chen, Samuel D. Stanton, Frances Ding +6
Although large language models (LLMs) have shown promise in biomolecule optimization problems, they incur heavy computational costs and struggle to satisfy precise constraints. On…
Antibody DomainBed: Out-of-Distribution Generalization in Therapeutic Protein Design
Nataša Tagasovska, Ji Won Park, Matthieu Kirchmeyer +8
Machine learning (ML) has demonstrated significant promise in accelerating drug design. Active ML-guided optimization of therapeutic molecules typically relies on a surrogate model…
Closed-Form Test Functions for Biophysical Sequence Optimization Algorithms
Samuel Stanton, Robert Alberstein, Nathan Frey +2
There is a growing body of work seeking to replicate the success of machine learning (ML) on domains like computer vision (CV) and natural language processing (NLP) to applications…