From algorithms to systems: integrating computation into drug discovery

Abstract

Introduction: Despite remarkable advances in computational methods, pre-clinical drug discovery continues to grapple with rising timelines and costs. Software, data, and automation are more powerful than ever, and increasingly these technologies are being embraced. However, there is still work to be done to translate this potential into meaningful reductions in cost and time. Areas covered: This perspective discusses the growth in drug discovery capability, exploring modern data infrastructure including cloud-native platforms, active learning, and laboratory automation. It covers emerging technologies such as LLM-based orchestration and emulation. Implementation examples illustrate successes and challenges. Expert opinion: AI presents an opportunity to envisage a new approach to drug discovery, but cultural and technological changes are required. The exponential growth in computational drug discovery tools requires solutions that enable researchers to access scalable and robust capabilities more easily. Data generation is usually the slowest and most expensive part of the design cycle; we advocate for a rigorous application of statistical methods focussing on learning efficiency from data over absolute predictive accuracy of models. Automation also plays a critical role in enabling rapid, high-quality data generation. Focussing on modular interoperable automated units with more attractive economics will drive much wider adoption.