Venture capital interest in AI-driven drug discovery has moved from “promise” to a more disciplined phase of investment & funding. Capital is still available, but it increasingly concentrates in teams and platforms that can prove (1) credible biology, (2) proprietary data advantage, and (3) a realistic path to value creation—whether through licensing, partnerships, or clinical progression. Recent industry tracking highlights a rebound in AI funding within drug R&D and emphasizes that “discovery engines” have captured a significant share of investment attention. This article explains the category in a knowledge-first way: what investors look for, common funding structures, due diligence priorities, and how startups can position themselves to raise responsibly. 1) Why This Category Attracts VC: The Economic Logic of “R&D Compression” Drug discovery is slow, high-attrition, and data-hungry. AI’s venture thesis is not “AI finds drugs automatically,” but that it can compress iteration loops and increase decision quality: Cycle-time reduction: faster design–make–test–analyze loops can reduce time to candidate selection. Higher information density: better prioritization of targets, compounds, or modalities can cut dead ends earlier. Platform scalability: once a system is built, it can (in principle) run multiple programs, partners, or indications. Investment trackers have described renewed…