AI modeling transforms drug development from a numbers game of screening millions of compounds to an engineering discipline. Researchers can model molecular systems upfront, understand key parameters, and design solutions for a specific problem, turning a costly screening process into a rapid, targeted design cycle.

By 2030, pharmaceutical companies are expected to double their product launches without a proportional increase in headcount or budget. This "grow without growing" pressure necessitates a fundamental shift towards technology-driven efficiency and productivity.

Martin Shkreli argues that the primary bottleneck in drug development isn't finding new molecules, but the immense inefficiency caused by poor communication, irrational decision-making, and misaligned incentives across numerous human departments. He believes AI's greatest contribution will be optimizing this complex organizational process rather than just improving discovery.

Eroom's Law (Moore's Law reversed) shows rising R&D costs without better success rates. A key culprit may be the obsession with mechanistic understanding. AI 'black box' models, which prioritize predictive results over explainability, could break this expensive bottleneck and accelerate the discovery of effective treatments.

The future of AI in drug discovery is shifting from merely speeding up existing processes to inventing novel therapeutics from scratch. The paradigm will move toward AI-designed drugs validated with minimal wet lab reliance, changing the key question from "How fast can AI help?" to "What can AI create?"

Despite scientific breakthroughs and better technology, the cost per approved drug has steadily increased over the last 60 years. This phenomenon, the reverse of Moore's Law, is called Eroom's Law and highlights a fundamental productivity problem in the biopharma industry, with costs approaching $1B+ per successful drug.

AI's primary value in early-stage drug discovery is not eliminating experimental validation, but drastically compressing the ideation-to-testing cycle. It reduces the in-silico (computer-based) validation of ideas from a multi-month process to a matter of days, massively accelerating the pace of research.

With over 5,000 oncology drugs in development and a 9-out-of-10 failure rate, the current model of running large, sequential clinical trials is not viable. New diagnostic platforms are essential to select drugs and patient populations more intelligently and much earlier in the process.

While most focus on AI for drug discovery, Recursion is building an AI stack for clinical development, where 70% of costs lie. By using real-world data to pinpoint patient locations and causal AI to predict responders, they are improving trial enrollment rates by 1.5x. This demonstrates a holistic, end-to-end AI strategy that addresses bottlenecks across the entire value chain, not just the initial stages.