How to measure the R&D productivity of an AI-powered drug discovery algorithm or company? скачать в хорошем качестве

How to measure the R&D productivity of an AI-powered drug discovery algorithm or company?

The AI drug discovery industry is often distracted by "Pinocchio walks"—fancy PowerPoints and star-studded boards that mask a lack of substance. If you want to measure true R&D productivity, stop looking at the hype and start looking at Zero to Developmental Candidate (PCC). Think of drug discovery like a high-performance sports car: The Acceleration (0 to PCC): This is where AI excels. Traditional methods take 4.5 years; we’ve shortened this to an average of 13 months. The Traffic (Clinical Trials): Once you hit IND-enabling studies, you are in a highly regulated zone. You must move at the "speed of traffic" dictated by regulators. You cannot "AI" your way out of a 28-day toxicity study. The Productivity Formula To compare platforms objectively, use this calculation: Productivity = Preclinical R&D Budget/Number of PCCs developed x Number of drugs out-licensed The Ultimate Lie Detector: Out-Licensing The "Number of drugs out-licensed" is the most critical variable. When a pharmaceutical company licenses your molecule, 70 experts spend months trying to "kill the deal" by finding flaws in your data. If you pass that rigorous scrutiny, your platform is validated. Benchmark Against Champions The real "China Speed" is the new global standard. Local biotechs there are discovering drugs at a scale and speed previously unseen. If you want to lead, don't just develop "fancy algorithms" for isolated problems—compete against the champions who are delivering high-quality candidates to the clinic every single year. Zero to PCC is the only benchmark that matters. Everything else is just moving with the speed of traffic.