Weekly AI/ML & Biotech Digest — Jul 6 to Jul 12, 2026
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 achieves state-of-the-art cell type classification and zero-shot disease state identification, even across species separated by 685 million years of evolution.
- ★ Autonomous biomedical research with an artificial intelligence agent — Introduces Biomni, a general-purpose biomedical AI agent that autonomously executes diverse research tasks. Its action-discovery agent mines tools, databases, and protocols from thousands of publications across 25 domains to build a unified agentic environment, integrating LLM reasoning with retrieval-augmented planning and code-based execution to compose workflows without predefined templates. Huang, Zhang, Wang et al., Science 2026.
- ★ How to build the virtual cell with artificial intelligence: Priorities and opportunities — Cells are essential to understanding health and disease, yet traditional models fall short of modeling and simulating their function and behavior.
- ★ A Chemically Defined Synthetic Cell Capable Of Growth And Replication — Cells are the fundamental unit of life.
- ★ MANNERS: A strategy for representation learning in multivariate datasets with high proportions of missing data
- ★ Generalist biological artificial intelligence in modeling the language of life — Generalist biological artificial intelligence (GBAI) represents a transformative approach to modeling the 'language of life'-the flow of information from DNA to cellular function.
- ★ Reshaping biomolecular structure prediction through strategic conformational exploration with HelixFold-S1
🤖 AI/ML
- ★ Accurate Structure-Property Understanding with Deep Native Structural Reasoning — Presents a deep native structural reasoning approach for structure-property relationships in biology, chemistry, and materials science. (morning-news-aiml, 2026-07-09)
- ★ Guiding generative models to uncover diverse and novel crystals via reinforcement learning — Abstract Discovering functional crystalline materials entails navigating an immense combinatorial design space.
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