6 papers
How to make the most of your masked language model for protein engineering
Calvin McCarter, Nick Bhattacharya, Sebastian W. Ober +1
A plethora of protein language models have been released in recent years. Yet comparatively little work has addressed how to best sample from them to optimize desired biological pr…
Is Sequence Information All You Need for Bayesian Optimization of Antibodies?
Sebastian W. Ober, Calvin McCarter, Aniruddh Raghu +4
Bayesian optimization is a natural candidate for the engineering of antibody therapeutic properties, which is often iterative and expensive. However, finding the optimal choice of…
Improved Therapeutic Antibody Reformatting through Multimodal Machine Learning
Jiayi Xin, Aniruddh Raghu, Nick Bhattacharya +3
Modern therapeutic antibody design often involves composing multi-part assemblages of individual functional domains, each of which may be derived from a different source or enginee…
Guided Sequence-Structure Generative Modeling for Iterative Antibody Optimization
Aniruddh Raghu, Sebastian Ober, Maxwell Kazman +1
Therapeutic antibody candidates often require extensive engineering to improve key functional and developability properties before clinical development. This can be achieved throug…
Bayesian Optimization of Antibodies Informed by a Generative Model of Evolving Sequences
Alan Nawzad Amin, Nate Gruver, Yilun Kuang +6
To build effective therapeutics, biologists iteratively mutate antibody sequences to improve binding and stability. Proposed mutations can be informed by previous measurements or b…
Generative Humanization for Therapeutic Antibodies
Cade Gordon, Aniruddh Raghu, Peyton Greenside +1
Antibody therapies have been employed to address some of today's most challenging diseases, but must meet many criteria during drug development before reaching a patient. Humanizat…