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PHARMACEUTICAL USE CASE
Architecting AI for Faster Drug Discovery

PHARMACEUTICAL USE CASE - Architecting AI for Faster Drug Discovery.png

1. Executive Summary

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A pharmaceutical company is currently implementing a structured, value‑driven AI transformation across its early discovery and preclinical R&D stages. A four‑member Enterprise Architecture (EA) team contributed to delivering deployable AI agents and workflows that:

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  • Reduced early‑stage target to investigational new drug (IND) cycle time by about 25%.

  • Increased drug candidate survival rates by 9 points so far.

  • Maintained total alignment with compliance, transparency, and fairness standards.

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This success wasn’t luck. It was architecture.

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2. Business Architecture: Grounding AI Using Value Streams

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From the outset, Business Architects mapped out grounded R&D value streams such as:

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  • Mine through target discovery & literature

  • Screen and rank in silico lead

  • Execute preclinical predictive toxicology

  • Design clinical cohort & protocol

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For each value stream, they defined shared information domains, like target profile, absorption-distribution-metabolism-excretion-toxicity (ADMET) properties, assay outputs, candidate ranking metadata, ensuring that everyone, from biologists to legal, speaks the same data language. This semantic consistency made all AI outputs explainable, auditable, and traceable during regulatory submission reviews.

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3. Enterprise Architecture: The 5‑Layer AI Blueprint

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Each enterprise architect in the pharmaceutical company assumed well-scoped responsibilities as shown in Figure 1 below:​

Figure 1 – EA Responsibilities for Each of the 5 AI-Layers.png

Enterprise Architecture oversight spans all layers. This prevents drift and ensures AI investments are strategically aligned and accountable for outcomes. 

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4. AI Agents Architecture: Modular, Explainable, and Safe

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Leveraging the “The Architecture of AI Agents” framework, the pharmaceutical company developed a modular ecosystem of valuable R&D AI agents, as shown in Figure 2 below:

Figure 2 – Developed R&D AI agents.png

​​​​Agents are orchestrated via dynamic task allocation, multimodal data handling, and built‑in monitoring. Every result triggers an audit trail and expert review. This architecture forms an adaptive ecosystem of learning modules that safely evolve with new data—exactly as envisioned by the AI agent architecture model.

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5. Project Execution: Five‑Phase Rollout

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Using a five‑phase AI implementation structure, the EA team assisted in driving delivery and execution with discipline and clarity. Each phase included a Go/No-Go checkpoint.

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i- Strategic Planning

  • Defined SMART goals (for example, cut hit-screen cost by 25% in 12 months)

  • Mapped sponsors (R&D, Data Science, Ethics, Legal)

  • Conducted knowledge & data audits; locked-in scope

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ii. Execution Blueprint

  • Selected models: docking nets, toxicity graph models

  • Designed pipeline: data flows, feature definitions, APIs, comp sizing

  • Set vendor vs in‑house roles, defined IP, DevOps, and budgets

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iii. Design & Development

  • Cleaned assays & knowledge graphs, aligned ontologies

  • Trained agents, captured model cards, and feature‑importance metadata

  • Ran bias tests and validation panels; deployed only after review

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iv. Deployment

  • Automated continuous integration and continuous delivery (CI/CD) into a hybrid cloud + on‑prem environment

  • Monitored performance, drift, latency, and fairness metrics in real‑time

  • Integrated outputs into downstream systems like contract development and manufacturing organization (CDMO) and quality assurance (QA) portals

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v. Continuous Improvement

  • Scheduled weekly stakeholder feedback, monthly retraining & user experience (UX) refreshes

  • Governance reviews with the Ethics Board and Audit teams

  • Tracked fairness metrics and system behavior transparently to leadership

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This structure prevented pilot paralysis and served to build trust rapidly.

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6. Outcomes & Measurable Value

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Here are the outcomes so far within 24 months:

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  • Early discovery to investigational new drug (IND) lead time: cut by about 6 months (or 25%)

  • Candidate survival rate: improved by 9% so far

  • Bias and compliance violations: very low and all rectified, as identified and measured in ethics dashboards

  • Model drift or hallucination events: flagged and remediated within SLAs

  • Governance transparency: live dashboards available to leadership and audit teams.

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These weren’t fanciful targets—they were tracked, measurable, and defensible in leadership forums.

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7. Why This Works

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  • Business‑led, not tool‑driven: Every agent, dashboard, or data model maps directly to a value stream or capability.

  • Ethics as architecture: Fairness, explainability, human approval gates, and auditability are foundational—not retrofit.

  • Agents built to evolve: Modular, explainable, continuously learning, and safe at scale.

  • Repeatable delivery: A five‑phase structure, gating checkpoints, stakeholder alignment baked in.

  • Governance as backbone: Four EAs directly aligned with compliance and data science, not siloed in IT.

 

This design doesn’t just accelerate R&D. It elevates trust, repeatability, and operational sustainability.

 

8- Final Thoughts

 

When enterprise architecture, business-led design, and ethical AI are combined, R&D becomes both faster and more reliable. Scientists can focus on science—not dashboards. Leadership gets real, repeatable impact—not guesswork. That’s how a pharmaceutical company went from “AI experiments” to transformative, board‑grade innovation—all by building with architecture and ethics at the core.

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