If you’re still discovering drugs and materials by trial-and-error, you’re burning capital. Chemical space is 10¹⁰⁰ large—yet most R&D screens less than 10⁻³⁰% of it. For over a century, enterprises relied on the Edisonian method: synthesize, test, fail, repeat. That approach delivered breakthroughs when complexity was low. Today, it’s mathematically and economically broken. 📉 The reality: • Drug discovery now costs $2.23B per asset • R&D IRR collapsed to 1.2% before modest recovery • Failure rates still hover near 90% • High-throughput screening explores a statistically meaningless fraction of chemical space 📈 The shift: Closed-Loop Autonomous Discovery—where AI predicts before chemistry begins. This whitepaper explains how leading enterprises are moving from guess-and-check to simulate-and-select by integrating: • Physics-Informed ML (PIML) instead of black-box AI • Bayesian Optimization over random screening • Active Learning flywheels that reduce experiments by 10–100× • Cost-informed optimization cutting reagent spend by up to 90% • Self-driving labs achieving in days what took humans years (41 novel materials in 17 days) 💡 Key insight: If a molecule, material, or reaction can be ruled out by simulation—but you still test it in a wet lab—you’re lighting money on fire. 🚀 What this means for enterprises: • Faster time-to-market • Predictable R&D economics • Permanent IP from negative data • AI systems that design, not just screen 📄 Read the full whitepaper to understand why the Edisonian era is ending—and how Closed-Loop AI is redefining discovery economics. 🔗 Whitepaper link in comments 📩 Want to assess how Closed-Loop Discovery fits your R&D roadmap? Email us at [email protected] or message us on WhatsApp: +91 9217059957 to start a technical conversation. Don’t guess and check. Simulate and select. #AIinR&D #DigitalDiscovery #EnterpriseAI #PharmaInnovation #MaterialsScience