Agents in Pharma — Source Library
Short descriptions for each linkable artifact behind the synthesis report "Are Agents Actually Working in Pharma?" Use these as the blurb/caption next to each download or link on the site.
The three briefings (PDF)
1. ChatGPT — Commercial Agentic Tools for Scientific Research and Laboratory Operations
A vendor-landscape briefing for pharma leadership. Its central argument: "agentic" does not yet mean fully autonomous wet-lab science at production scale. It splits the market into two layers — horizontal frontier-model platforms (OpenAI, Anthropic, Google, Microsoft) acting as the orchestration "control plane," and science-native platforms (Benchling, Sapio, Causaly, Synthace, Laboperator, Asimov, Scispot) that supply structured scientific context, ELN/LIMS objects, and instrument connectivity. Includes a capability matrix across the use cases pharma leaders actually ask about (planning, experiment design, protocol execution, ELN/LIMS integration, instrument control, data analysis, evidence search) and a launch timeline. Take-home for buyers: deploy a governed stack with human-in-the-loop, not the most autonomous agent. Best for: tooling/vendor selection and procurement strategy.
2. Perplexity — AI Agents in Science & the Laboratory: A Pharma Leadership Briefing
The market-and-evidence briefing. Quantifies the moment — 173 AI-discovered programs in active clinical development, a ~$5.1B AI-drug-discovery market in 2026 (23–25% CAGR), pharma as the #1 sector for AI-agent adoption (23%) — and walks vendor-by-vendor through Anthropic (Claude for Life Sciences), OpenAI, Google (AlphaFold 3 / Vertex), Microsoft, AWS, NVIDIA (BioNeMo) and Benchling. Crucially, it pairs the enthusiasm with a frank "Integrity and Reliability Crisis" section (FDA's hallucinating Elsa tool, KPMG's retracted agentic study, agents ignoring evidence 68% of the time) and the new FDA/EMA joint "Good AI Practice" principles (Jan 2026).Best for: the strategic state-of-the-market and the risk/governance picture.
3. Subindex — A One-Day, AI-Assembled Update to BCG's AI-Drug-Discovery Analysis
The outcomes evidence base — and our own work. A citation-backed, 28-month re-enumeration of the landmark BCG/Jayatunga cohort (Drug Discovery Today, 2024), rebuilt in ~one day on Subindex.AI: 83 clinical assets × 16 fields = 1,328 confidence-coded research queries. Headline findings: Phase I ~78% (58 events), Phase II ~41% (22 events), Phase III now has signal (67%, 3 events); end-to-end probability of success ~16–19% vs a 5–10% industry baseline — BCG's "almost a doubling of R&D productivity" survives the update. Its sharpest result: AI involvement collapses after preclinical (mean AI-use score 3.16 → 0.49 across the five development stages), and only 2 of 83 assets are "AI-shaped end-to-end."Best for: the hard question of whether AI is changing clinical outcomes.
The two underlying Subindex tables (interactive )
4. Leading Pharma Adoption of Agentic AI Tools — 65 companies
A Subindex agentic table mapping how the 65 leading and AI-native pharma companies are deploying agents: initiative, adoption stage, primary use case, technology partners, and reported impact. 320 cells, 83% mean confidence (224 High / 85 Medium / 11 Low), 619 sources cited of 741 examined. Shows that ~63% are already in active internal or scaled use, that two-thirds of initiatives target drug discovery, and that the infrastructure "control plane" is dominated by NVIDIA, AWS, Microsoft and Google. Live viewer:https://subindex.ai/viewer?demo=leading-pharma-adoption-of-agentic-ai-tools-239cd1
5. Agentic Tools for Pharma — 27 tools
A Subindex agentic table profiling 27 agentic tools/platforms (Claude for Life Sciences, Benchling AI, Recursion OS, BioNeMo, AlphaFold 3, Causaly, IQVIA.ai, Owkin, plus emerging agents like Kosmos, AutoTrial and Medra.ai) across core agentic capabilities, scientific benchmarks, pharma/lab integrations, regulatory response, compliance posture, known adopters and academic mentions. 234 cells, 72% mean confidence (110 High / 115 Medium / 9 Low), 124 sources cited of 344 examined — the lower confidence reflects how much of the agentic-tool market is still pre-GA and thinly evidenced. ~78% of tools claim HIPAA / SOC 2 / GxP posture. Live viewer:https://subindex.ai/viewer?demo=table-tool-name-fcb717
The synthesis report
6. Are Agents Actually Working in Pharma? A Cross-Source Read
A single, figure-driven synthesis that reconciles all five sources above into one answer to two questions: how are agents being used in pharma, and is it producing better outcomes? The convergent finding — visible independently in the 65-company adoption table, the BCG stage scores, and the ChatGPT capability matrix — is a sharp discovery-to-clinic gradient: adoption is real and broad, but agent capability, investment and measurable effect concentrate at drug discovery and thin out toward the clinic and the regulator. The early outcomes signal is genuine (a near-doubling of early-stage productivity; the first AI clinical proof-of-concept and the first AI-discovered FDA approval) but narrow and not yet downstream — and the binding constraint is trust and governance, not raw model capability. Five take-home bullets, four figures, and a clearly labelled source basis. Best for: the one-page executive read that ties the library together.
AI Agents in Science and the Laboratory: Research Collection
This collection brings together several structured research outputs on how AI agents are beginning to show up in scientific work, pharma R&D, and laboratory operations. The goal is to give leadership a fast orientation to the agentic tools now becoming commercially accessible, how they are being used in practice, and how the scientific and technical communities are responding.
ChatGPT Deep Research - Market Landscape and Executive Synthesis — A crash-course report on commercially accessible agentic tools for science and laboratory workflows, including offerings from major AI platforms, scientific software companies, lab automation providers, and emerging “AI scientist” systems.
Perplexity Deep Research Report — A broad web-grounded scan of AI agents in scientific research and laboratory settings, with emphasis on current capabilities, adoption signals, and open questions for pharma and biotech leaders.
Link: [Add link]Subindex: Pharma/User Perspective — Structured intelligence from the perspective of pharma teams and scientific users, highlighting examples of how different organizations are experimenting with agents across research, analysis, documentation, and lab-adjacent workflows.
Link: [Add link]sub-index.ai: Tool Perspective — A tool-by-tool view of the agentic science landscape, focused on available platforms, what they do, how they are being used, and where they appear to fit in the emerging scientific AI stack.
Link: [Add link]