Weekly AI/ML & Biotech Digest — Aug 10 to Aug 16, 2026

Curated weekly digest of notable AI/ML and biotech developments. Flagged items are manually selected; remaining items are the week's most recent.

🧬 Biotech

• ★ ViMax: Agentic Video Generation — Long-form video generation requires systematic narrative planning and visual consistency that current short-clip methods cannot provide.
• ★ Decoding intercellular activities for more than 1,000 secreted proteins
• ★ A light-controlled tetanus neurotoxin enables rapid and reversible synaptic inhibition in vivo
• ★ Alignment with experimental data improves protein generative modeling
• ★ Efficient evidence-based genome annotation with EviAnn — Machine-learning-based ab initio gene finders have long been central to eukaryotic genome annotation, largely because gene expression data were historically expensive and limited.
• ★ ClairS: a deep-learning method for long-read tumor–normal pair somatic small variant calling — Somatic variant discovery in tumors is crucial for clinical analysis, yet most existing methods are designed for short-read sequencing, with few developed specifically for long reads.
• ★ Proteome-wide identification of the druggable CRBN interactome — Abstract Molecular glue degraders (MGDs), such as pomalidomide, induce degradation of non-native substrates by the cullin-RING E3 ligase 4 (CRL4) through its substrate receptor cereblon (CRBN).
• ★ A trimodal protein language model enables advanced protein searches — ProTrek unifies protein sequence, structure and natural language function in a trimodal language model through contrastive learning, enabling comprehensive searches between any two modalities, including within modality.
• ★ Discovery and protein language model-guided design of hyperactive transposases — The diversity and biochemical potential of the PiggyBac transposase gene insertion system remains largely unexplored.
• ★ Mapping and engineering the human cell–cell interactome — Efforts to systematically understand how cell interactions tune tissue-level function have motivated transformative advances in single-cell transcriptomics and spatial profiling.