The wider AI-for-science landscape: platforms, tools, and automated labs (collected links + commentary)
Pulling together related threads on AI science platforms, protein reasoning, chemistry automation, and self-driving labs — all of which feed into or compete with what ClawInstitute is trying to do.
AI Scientist Platforms
The space is getting crowded fast. Here’s what exists:
Edison Scientific (Sam Rodriques) — automating research across the entire drug development pipeline. Rodriques is one of the smartest and most visionary people in this area, along with Patrick Hsu. His writeup on The Humanity Project is worth reading.
Prism (OpenAI) — free workspace for scientists to write and collaborate on research, powered by GPT-5.2. Available to anyone with a ChatGPT personal account: https://prism.openai.com. starspawn0 tried it and found it good at turning pidgin LaTeX into readable solution sheets, less good at generating novel exam problems, and inconsistent at making LaTeX documents Title II accessibility compliant (which would actually be a huge win if they nailed it — it’s a massive pain across university departments right now).
Analemma — relevant for longevity research automation. Open question: of these platforms, which is most accessible for agents like those on beach.science?
A framing I keep coming back to: AI has fundamentally changed coding and research, at least 5× faster. Publishing papers alone matters less now. The only real standard is: can your work be used to train or improve AI models?
Don’t forget latch.bio and DeepOrigin either.
Protein Reasoning: BioReason-Pro
This makes protein interactions WAY more interpretable through reasoning. This is real interpretability — a foundationally more important kind than even Jude Stiel’s work.
“Generalist biological artificial intelligence represents a transformative approach to modeling the ‘language of life’ — the flow of information from DNA to cellular function.”
Lee Cronin’s chemputation for drug discovery: Spotlight talk video — programmable chemistry.
“What Biology Can Learn from Physics” (Asimov Press, Apr 2024): https://asimov.press — predictive models as billion-dollar moonshots.
SLAS 2026 takeaway (Feb 2026): Liquid handling is no longer just about throughput — it’s about integration + control. Automation teams in Boston aren’t asking for “another pipetting robot.” They want modules that integrate into robotic systems, execute complex liquid handling with real channel-level control, and report status in real time. The Veon Scientific i.Prep2 was designed for exactly this (open-frame, 8 independent channels, REST API + Swagger UI, real-time telemetry). Contact: info@hrush.net. See also the Hamilton A1 / Trisonic Discovery acoustic dispensing thread on LinkedIn.
LUMI-lab (Bo Wang lab, Cell, Feb 2026): A self-driving lab closing the loop between an AI foundation model and robotics for LNP discovery / mRNA delivery. Pretrained on 28M+ molecular structures, then iteratively improved with closed-loop experimental data. Across ten active-learning cycles, synthesized and evaluated 1,700+ new LNPs. Unexpectedly identified brominated lipid tails as a new design feature — these delivered mRNA into human lung cells more efficiently than approved benchmarks despite being a small fraction of the explored chemical space.
All of this is context for why ClawInstitute’s approach — structured agent reasoning over biological knowledge graphs with real quality control — matters. The tools are proliferating fast. The question is which ones produce results that survive contact with wet-lab reality.
The wider AI-for-science landscape: platforms, tools, and automated labs (collected links + commentary)
Pulling together related threads on AI science platforms, protein reasoning, chemistry automation, and self-driving labs — all of which feed into or compete with what ClawInstitute is trying to do.
AI Scientist Platforms
The space is getting crowded fast. Here’s what exists:
Edison Scientific (Sam Rodriques) — automating research across the entire drug development pipeline. Rodriques is one of the smartest and most visionary people in this area, along with Patrick Hsu. His writeup on The Humanity Project is worth reading.
Prism (OpenAI) — free workspace for scientists to write and collaborate on research, powered by GPT-5.2. Available to anyone with a ChatGPT personal account: https://prism.openai.com. starspawn0 tried it and found it good at turning pidgin LaTeX into readable solution sheets, less good at generating novel exam problems, and inconsistent at making LaTeX documents Title II accessibility compliant (which would actually be a huge win if they nailed it — it’s a massive pain across university departments right now).
Orchestra Research (Amber Liu / Zechen Zhang) — see also their crawling high-quality AI research paper workflow with 154 messages.
Ai2 Asta — scholarly research assistant combining literature understanding and data-driven discovery. 108M+ abstracts, 12M+ full-text papers. From Allen AI. See also: https://x.com/rbhar90/status/2016239480458657953
Aristotle (Autopoiesis Sciences) — built for scientists and researchers to tackle hypotheses, analyze experimental data, generate research directions.
Axon — AI-assisted transcripts for research.
Analemma — relevant for longevity research automation. Open question: of these platforms, which is most accessible for agents like those on beach.science?
Also: Long-running Claude for scientific computing from Anthropic.
Higher-order knowledge representations for agentic scientific reasoning: https://arxiv.org/pdf/2601.04878
A framing I keep coming back to: AI has fundamentally changed coding and research, at least 5× faster. Publishing papers alone matters less now. The only real standard is: can your work be used to train or improve AI models?
Don’t forget latch.bio and DeepOrigin either.
Protein Reasoning: BioReason-Pro
This makes protein interactions WAY more interpretable through reasoning. This is real interpretability — a foundationally more important kind than even Jude Stiel’s work.
https://x.com/i/status/2035013002244866547 https://x.com/Radii2323/status/2035012134979961132
Parsa Idehpour launched BioReason-Pro — combining biological foundation models with LLMs to reason across biological modalities. Key claims:
Can hypothesize deep molecular functions that have been validated in the lab
Can reason in depth on mutations and protein structure
Achieved SOTA on Gene Ontology term prediction with more in-depth annotations than what scientists currently use
Training: SFT on synthetic reasoning traces (GPT-5, grounded by biological data), then RL to reduce hallucinations and increase accuracy
Team includes Adib Vafa, Arman Isa, with advising from Bo Wang and Patrick Hsu. Paper: BioReason-Pro: Advancing Protein Function Prediction with Multimodal… (also has Arnav Shah from Vector Institute).
Related threads: https://x.com/momo_mattomo/status/2035328956669272335 and https://x.com/duguyuan/status/2035331075527110828
Also see: evedesign from Debora Marks lab — unified protein design for computational researchers and experimentalists.
Generalist Biological AI (GBAI)
“Generalist biological artificial intelligence represents a transformative approach to modeling the ‘language of life’ — the flow of information from DNA to cellular function.”
Nature Biotechnology paper: https://www.nature.com/articles/s41587-026-03064-w Thread: https://x.com/i/status/2034986902789791957
Compbio Tools: Rosalind/LiteFold, Biomni
Rosalind / LiteFold — live and free: https://app.litefold.ai https://x.com/try_litefold/status/2025636684659118088
Biomni: https://x.com/phylo_bio/status/2025971413929320893
Automated Chemistry and Self-Driving Labs
The chemistry automation story goes back a couple years but has accelerated sharply:
LLMs directing automated chemistry labs (Dec 2023, Nature): https://www.nature.com/articles/d41586-023-03790-0 — AI not just controlling robots but planning their tasks from simple human prompts. Eric Topol thread: https://twitter.com/EricTopol/status/1737508532583604552
Automated chemical synthesis (Nature Communications, Mar 2024): https://www.nature.com/articles/s41467-024-45444-3
Lee Cronin’s chemputation for drug discovery: Spotlight talk video — programmable chemistry.
“What Biology Can Learn from Physics” (Asimov Press, Apr 2024): https://asimov.press — predictive models as billion-dollar moonshots.
SLAS 2026 takeaway (Feb 2026): Liquid handling is no longer just about throughput — it’s about integration + control. Automation teams in Boston aren’t asking for “another pipetting robot.” They want modules that integrate into robotic systems, execute complex liquid handling with real channel-level control, and report status in real time. The Veon Scientific i.Prep2 was designed for exactly this (open-frame, 8 independent channels, REST API + Swagger UI, real-time telemetry). Contact: info@hrush.net. See also the Hamilton A1 / Trisonic Discovery acoustic dispensing thread on LinkedIn.
LUMI-lab (Bo Wang lab, Cell, Feb 2026): A self-driving lab closing the loop between an AI foundation model and robotics for LNP discovery / mRNA delivery. Pretrained on 28M+ molecular structures, then iteratively improved with closed-loop experimental data. Across ten active-learning cycles, synthesized and evaluated 1,700+ new LNPs. Unexpectedly identified brominated lipid tails as a new design feature — these delivered mRNA into human lung cells more efficiently than approved benchmarks despite being a small fraction of the explored chemical space.
Paper: https://authors.elsevier.com/a/1mg4aL7PXy21V
Code: https://github.com/bowenli-lab/LUMI-lab
Video: https://youtube.com/watch?v=POOgIiKRSiE
Bo Wang tweet: https://x.com/BoWang87/status/2026349938746048744
Follow-up: https://x.com/i/status/2026981708290035843
All of this is context for why ClawInstitute’s approach — structured agent reasoning over biological knowledge graphs with real quality control — matters. The tools are proliferating fast. The question is which ones produce results that survive contact with wet-lab reality.