Precision Drug Discovery: 4-Week PoV

Life sciences organizations face pressure to accelerate discovery timelines, yet manual processes and siloed research data slow evidence-gathering and limit how many candidates get evaluated. Manual processes limit the speed and scale of scientific discovery.

MAQ Software delivers a precision discovery capability on Microsoft Azure Machine Learning and Microsoft Fabric that unifies research data, scores candidates, and gives scientists a grounded Microsoft Copilot, back-tested against outcomes you already know.

Business Challenge

• Genomic, proteomic,and experimental data sit in separate systems, so assembling evidence behind a target takes weeks and prior work stays undiscoverable.
• Manual screening is slow and expensive, limiting how many molecules teams evaluate before committing wet-lab time.
• Generative AI often runs without the governance regulated research demands.

Key Questions

• Can your scientists discover data and models in one place instead of across silos?
• Can you score candidates to avoid expensive wet-lab cycles?
• Do your historical results support reliable prediction, and have you tested that?
• Is your compound and sequence data governed and kept in your tenant?

Strategy

Week 1:

• Select one disease area and program with named scientists.
• Select the validation protocol and success criteria, inventory data sources, and provision Microsoft Azure Machine Learning and Microsoft Fabric with Microsoft Entra ID and Microsoft Purview.

Week 2:

• Build the unified data foundation in Microsoft Fabric, bringing multi-omics, assay, and experimental data into OneLake as one place to discover data and models.
• Index literature in Microsoft Azure AI Search.

Week 3:

• Train candidate scoring and property models in Microsoft Azure Machine Learning on your historical screening data.
• Build a grounded research Microsoft Copilot on Microsoft Azure OpenAI answering from unified data with citations. Register models with lineage.

Week 4:

• Back-test scoring and predictions against a held-out set of outcomes your teams already know.
• Scientist review of ranked candidates and Microsoft Copilot responses. Configure governance, then deliver handover and roadmap.

Deliverables

• Working precision discovery capability in your Microsoft Azure environment, scoped to one disease area
• Candidate scoring validated by back-test against known outcomes
• Grounded research Microsoft Copilot answering from unified data with citations
• Unified Microsoft Fabric data foundation and registered Microsoft Azure Machine Learning models with lineage

Target Audience

• Research Scientists
• Computational Biologists and Chemists
• Bioinformaticians

Business Outcomes

• Scientists find the research data they need in one place, instead of searching across separate systems.
• Prioritize the most promising molecules, reducing time and cost spent on wet-lab testing.
• Proof that the prediction actually works, checked against results your team already knows.
• A clear record showing which data supported each recommendation, for audit and compliance.

Why MAQ Software

• Proven expertise unifying genomic, proteomic, and experimental data for discovery teams.
• Deep experience in validating candidate scoring against your historical data.

Call To Action

• Reach out to CustomerSuccess@maqsoftware.com
• Learn more about our experience in Healthcare & life sciences