Precision Drug Discovery: 4-Week Proof of Concept

OVERVIEW:

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?

AGENDA:

Week 1:

Select one disease area and program with named scientists. Agree 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.

SOLUTION / APPROACH:

Use Case 1:

Candidate Scoring and Ranking: A drug candidate is a molecule considered as a potential medicine, and wet-lab work is physically testing it, which is slow and costly. Models trained on your assay data rank candidates by predicted success, so scientists prioritize the most promising molecules for wet-lab testing instead of testing every candidate blindly.

Use Case 2:

Grounded Research Microsoft Copilot: A Microsoft Copilot on Microsoft Azure OpenAI and Microsoft Azure AI Search lets scientists query unified research data, protocols, and prior results in natural language, with every answer cited to its source.

Data Foundation:

Microsoft Fabric unifies multi-omics, assay, and experimental data in OneLake across raw, curated, and harmonized zones, giving teams one place to discover data and models. Microsoft Azure AI Search retrieves literature, with lineage preserved so results trace to source.

Governance and Responsible AI:

Models and the Microsoft Copilot inform prioritization; scientists decide what advances. Your compound and sequence data stays in your tenant and is never used to train foundation models. Microsoft Purview classifies sensitive intellectual property and Microsoft Azure Machine Learning records model versions and lineage for audit.

DELIVERABLES:

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

Users:

• Research Scientists
• Computational Biologists and Chemists
• Bioinformaticians

BUSINESS OUTCOMES:

1. Scientists find the research data they need in one place, instead of searching across separate systems

2. Prioritize the most promising molecules, reducing time and cost spent on wet-lab testing.

3. Proof that the prediction actually works, checked against results your team already knows.

4. A clear record showing which data supported each recommendation, for audit and compliance

WHY MAQ SOFTWARE:

• Life Sciences research domain depth: We have unified multiple genomic, proteomic and experimental data for discovery teams, harmonizing sources that were never designed to sit in one model.
• Back-tested before you commit: Candidate scoring is validated against outcomes your scientists already know, whether prediction works on your data before funding rollout.
• Own your Intellectual property: Compound and sequence data remains in your tenant and is never used to train foundation models.

CALL TO ACTION:

• Reach out to CustomerSuccess@MAQSoftware.com.
• Learn more at MAQ Software Healthcare & life sciences.