C2Bio

Curated weekly digest of notable AI/ML and biotech developments. Flagged items are manually selected; remaining items are the week's most recent. 🧬 Biotech ★ AI-redesigned starting points and outcomes enhance protein evolution — Abstract Engineered or laboratory-evolved proteins often have suboptimal stability, activity or specificity. ★ Distributional regression using generalized additive models for location, scale and shape ★ High-throughput machine learning-aided antibody discovery for cell surface antigens — Machine learning (ML) has the potential to revolutionize antibody design and selection, but its success depends on access to well-curated datasets of antibody-antigen interactions.

Curated weekly digest of notable AI/ML and biotech developments. Flagged items are manually selected; remaining items are the week's most recent. 🧬 Biotech ★ Biotech's digital twin dilemma — Nature Biotechnology explores how digital twins — from virtual patients to simulated cells and organs-on-chips — are forecasting disease and guiding drug development. ★ Universal cell embedding provides a foundation model for cell biology — A self-supervised single-cell foundation model (>650M-parameter, 33-layer transformer) trained on 36M cells across eight species, producing a 1,280-dimensional universal embedding that enables zero-shot cross-species cell-type mapping without fine-tuning or annotation. ★ De novo design of orthogonal far-red, orange, and green fluorophore-binding proteins for multiplexed imaging — Fluorescent proteins and small-molecule dyes offer complementary advantages for biological imaging: proteins are amenable to genetic tagging, whereas dyes pr...

Curated weekly digest of notable AI/ML and biotech developments. I really liked the paper " Guiding generative models to uncover diverse and novel crystals via reinforcement learning" by  Hyunsoo Park & Aron Walsh, published in Nature Machine Intelligence. They leveraged RL to guide the generative model to generate novel crystals that differ from the training data distribution, which also showed more stable training behavior. Traditionally model generate new data by sampling in latents space close to the original training data, which limits novelty, and may even hinder the discovery of entirely new structures that are no longer subject to evolutional selection. Very nice work.  Although I do not work on crystal structures, I do feel their work is inspiring for bio-centered AI/ML applications. 🧬 Biotech ★ TranscriptFormer: A generative cell atlas across 1.5 billion years of evolution — A generative foundation model trained on 112 million cells across 12 species ach...

I am starting a series of weekly digest of notable AI/ML and biotech developments related to my own interests. While most of the contents here are published in the past week, I may also add papers that were published earlier but just got my attention. I hope you will find these weekly digests useful. 🧬 Biotech ★ Multivalent mRNA vaccine platform with compatible antigens conferred broad-spectrum protection against orthoebolaviruses' exposure — A multivalent mRNA vaccine platform delivering glycoproteins and nucleoproteins in a single LNP for broad-spectrum protection against EBOV, SUDV, and BDBV. (PNAS) ★ Biomolecular profiling for noninvasive health monitoring — A review highlighting how MS-based molecular discovery and wearable sensing serve as complementary approaches for continuous, noninvasive health monitoring. (Nature Biotechnology) 🤖 AI/ML ★ Evaluating Large Language Models in Scientific Discovery — A scenario-grounded benchmark evaluating LLMs across bi...