Weekly AI/ML & Biotech Digest — Oct 12 to Oct 18, 2026

Curated weekly digest of notable AI/ML and biotech developments. 

This week's most exciting article may be RFDiffusion 3 paper from Baker's lab. Their preprint on bioRxiv was posted on November 19, 2025--so almost one year before it was accepted by Science. 

https://www.science.org/doi/10.1126/science.aeh2982

Original preprint: https://www.biorxiv.org/content/10.1101/2025.09.18.676967v2.full.pdf

Some other paper interesting papers: 

• ★ A high-resolution protein-RNA binding atlas for structure-aware interaction prediction and post-transcriptional regulatory modeling — RNA-binding proteins (RBPs) control post-transcriptional gene expression by regulating RNA processing, localization, stability, and translation.
• ★ Mutational constraints on RSV F and its neutralization by antibodies — New antibodies targeting the F protein of respiratory syncytial virus (RSV) have substantially reduced infant hospitalizations 1 .
• ★ BioMod: Biological Module Tokenization for Single-Cell Foundation Modeling — Introduces biological module tokenization for single-cell foundation models, inferring higher-order biological organization directly from sparse expression profiles. (morning-news-biotech, 2026-10-14)
• ★ STAMP as a topology-aware molecular language for fragment-level learning and editing — Learned representations capture motif similarity and recurring transformations, and lower activity-cliff prediction error on 23 of 30 targets versus a matched SMILES encoder (arxiv-digest, 2026-10-13)
• ★ ProTok: Unified Protein Tokenization via Modular Multi-Codebook Quantization — A single ProTok checkpoint supports flexible inference from any modality subset without retraining (arxiv-digest, 2026-10-13)
• ★ Programmable design of functional RNAs through multimodal generative learning — RNA engineering is bottlenecked by the tight coupling among sequence, structure, and function, leaving the field reliant on heuristics or screening.
• ★ Dual-graph deconvolution of enzyme fitness: an interpretable framework to identify and explain catalytic modulators — Models trained on protein sequences can predict mutational effects, but their outputs represent a convolution of different phenotypes.
• ★ Joint Discrete and SE(3) Diffusion for Protein Interface Design — The design of high-affinity, target-specific protein binders is central to the development of modern therapeutics.
• ★ CLEAN Database: providing enzyme function coverage for over 55 million sequences — Enzyme function annotation is critical for decoding biological systems and enabling rational enzyme design.
• ★ Programming biological behavior with AI-guided de novo protein design