Drug discovery is not constrained by a lack of data but by the difficulty of synthesizing it into reliable scientific judgment. Target assessment, prioritization, and on-target safety depend on interpreting fragmented, incomplete, and often contradictory biological evidence, work that remains largely manual and difficult to scale.

In this session, Manas AI and Causaly demonstrates how agentic research introduces a different operating model for early discovery. Rather than using AI solely for prediction or retrieval, agentic systems are designed to carry out structured scientific reasoning: gathering evidence, evaluating hypotheses, and navigating whitespaces across complex biological contexts.

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When:

📅 Date: March 12th, 2026

⏰ Time: 4 PM BST | 8 AM PDT | 11 AM EDT

⏳ Duration: 45 minutes

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In this session, you will:

• Understand why scientific judgment and not just data volume is the limiting factor in early discovery

• How Manas AI and Causaly combine complementary strengths to improve discovery outcomes

• See how autonomous agents support target assessment, prioritization, and on-target safety

• Explore how structured, adaptable workflows balance scalability with biological nuance

• Gain insight into how graph-based intelligence surfaces evidence, uncertainty, and white spaces