For Biopharma innovation leaders, the near-term value in drug discovery does not lie in more use of AI but with faster, higher-confidence portfolio decisions taken upstream. Such critical scientific decisions include optimal target identification , prioritisation , determination of mechanims of action ( MoA) . If these decisions are taken early with justifiable scientific rationale and evidence , downstream cost and time-to-action are minimised . To facilitate such decisions , conventional GenAI toolkits are insufficient as they do not capture the scientific nuances associated with biomedical workflows involved in drug discovery and development.
In contrast , biomedical domain-specific GenAI can convert fragmented internal and public biomedical knowledge into a governed, reusable discovery capability that reduces data mining and analytics effort, shortens hypothesis-to-experiment cycles limits and reduces wet-lab rework across therapeutic areas.
Indeed, with general-purpose AI, firms continue to confront execution friction. In a 2025 analysis of the top 20 biopharma companies, the forecast R&D return improved to 5.9%, but the average cost per asset reached $2.23 billion and Phase III cycle times increased 12%, underscoring why productivity gains must come from better decisions earlier in the pipeline.
While GenAI adoption is on the rise – in a 2026 survey by NVIDIA, 69% of respondents reported using GenAI and large language models for drug discovery (up from 54% the prior year) – fundamental gaps in value creation surface in upstream workflows where discovery teams need domain-ednabled GenAI embedded in biomedical reasoning, evidence traceability and multi-hop scientific hypothesis generation.
Upgrading Domain GenAI from Assistant to Discovery Capability
Biopharma funding reached a ten-year high in 2024 (excluding the pandemic peak); however, clinical trial starts remained flat at over 5,300. This indicates cycle time and productivity remain key battlegrounds. Considering around 87% of alliance investment has focused on AI platforms to accelerate R&D, pointing to continued emphasis on embedding AI into clinical workflows with little to show for it.
Most GenAI pilots deliver impressive demos but demonstrate limited enterprise impact. This is because they lack trusted data foundations and access controls, evidence-grounded outputs that stand up to rigorous scientific review, measurable quality and productivity KPIs and workflow integration that influences how teams make decisions. The differentiator is an operating model—governance, evaluation and reuse—that turns AI models into portfolio-grade decision assets rather than one-off tools.
In this environment, competitive advantage comes from turning proprietary data into faster, higher-quality decisions; not from isolated AI pilots. For AI to accelerate R&D cycles or go-to-market timelines for new drugs, it must be fine-tuned with appropriate biomedical datasets and deeply modeled for processes that occur closer to drug discovery.
Strategic Technology Expertise for Domain GenAI at Scale
Persistent designed and operationalized a GenAI-powered early discovery platform on Google Cloud, turning fragmented biomedical knowledge into an enterprise-grade decision engine for target identification, MoA exploration and evidence-backed hypothesis generation. Focusing on moving beyond pilots to a scalable operating model, Persistent enabled discovery teams to trust AI and reuse it across therapeutic areas.
At the core of our approach, we fine-tuned open weight AI Foundation models by establishing a comprehensive pipeline with secure data ingestion, curation and governance, evaluation and workflow integration. These foundation models were fine tuned with:
- Proprietary & public biomedical data: We sourced experimental data, internal reports and curated public sources (e.g., literature, biobanks, research repositories) to strengthen disease-context reasoning
- Data engineering & governance for regulated R&D: We implemented ingestion, enrichment, quality controls, access policies and lineage to support auditability and re-use
- Fine-tuning & preference optimization with rigorous evaluation: We supervised the fine-tuning of Google’s open-weight, domain-specialized models – MedGemma and TxGemma – with biomedical benchmark testing and evidence-grounding via our proprietary biomedical knowledge graph – Pi-OmniKG
- Workflow integration for scientists: We embedded copilots and search experiences into discovery workflows to reduce manual literature triage and accelerate hypothesis-to-experiment decisions
With these workflows , Persistent successfully upgraded biomedical GenAI from a generic assistant into a client-owned, IP-grade discovery capability, which is purpose-built for disease-specific reasoning, multi-step evidence synthesis and reproducible scientific decision-making.
Faster Cycles, Lower Wet-Lab Rework, Higher-Confidence Decisions
Persistent brought the Biopharma R&D domain depth, data-and-AI engineering rigor and governance-first approach needed to convert foundation models into enterprise discovery assets that can be trusted by scientists and governed by leadership. We delivered:
- Deep BioPharma R&D, translational science and data engineering expertise to align GenAI outputs to real discovery decisions
- Proven fine-tuning and evaluation workflows designed for regulated, science-first environments (quality, traceability and repeatability)
- Responsible GenAI by design with evidence grounding, confidence signals and human-in-the-loop controls to reduce hallucinations and improve trust
- Implementation on Google Cloud’s AI stack with integration into the Google Research ecosystem to enable performance, scalability and future extensibility
The MedGemma and TxGemma models are now helping preclinical R&D teams reduce time spent on evidence gathering, increase hypothesis quality and limit costly experimental iterations, witnessing:
- Up to 60% reduction in discovery analysis effort for uncovering complex biological relationships, accelerating target/pathway identification and prioritization
- ~50% reduction in time and cost by reducing trial-and-error cycles via AI-assisted synthesis, ranking and recommended next-best experiments
- 50% improvement in research outcomes driven by higher-confidence, evidence-backed insights supported by benchmarking and validation frameworks
For Pharma leadership, the value is fewer R&D cycles spent validating weak hypotheses, faster convergence on high-confidence targets and a reusable evidence layer that improves decision consistency across multiple programs. Success can be governed with clear KPIs, such as time-to-insight, evidence coverage/traceability, experiment rework rate and adoption by priority TAs. Our model-fine tuning pipeline can be easily scaled through a phased rollout, starting with 1–2 high-impact discovery decisions, establishing evaluation and compliance controls, then expanding to adjacent workflows and additional therapeutic areas as the knowledge foundations expand continously.
