Grand Challenges for Predictive Modeling in Small Molecule Drug Discovery

We present a set of Grand Challenges for predictive modeling in small molecule drug discovery, with the goal of defining, prioritizing, and quantifying the areas where computation can have transformative impact. Rather than offering another broad survey of methods, this paper articulates specific scientific and technical problems that limit progress today and proposes measurable criteria by which advances can be judged. Our objective is to align researchers, investors, and industry leaders around the challenges that matter most for advancing real drug discovery programs. This work seeks to broaden participation in drug discovery by providing a clear roadmap for contributors from the growing areas of artificial intelligence, machine learning, robotics, high-performance computing, and quantum computing, where tools are rapidly advancing but are often disconnected from the practical realities of medicinal chemistry and pharmacology. The insights presented here draw on extensive discussions with experienced drug hunters, computational method developers, and thought leaders across biotech, pharma, software, and venture capital, as well as lessons learned from our own drug discovery efforts. While there is substantial enthusiasm (particularly around AI) for revolutionizing drug discovery, this moment demands sharper problem definition. Without clearly articulated challenges and performance metrics, computational innovation risks optimizing for benchmarks rather than for translational impact. We therefore identify Grand Challenges across four domains: Chemistry, Structure, Energy, and Pharmacology. For each domain, we present a common framework: a well-defined challenge, the underlying physical principles, its relevance to drug discovery decision-making, the current state of the field, and quantitative metrics that define progress. By grounding computational ambition in concrete scientific problems, we aim to catalyze sustained, measurable advances rather than episodic waves of enthusiasm.