Computational strategies for allosteric drug discovery: from cryptic pocket detection to rational design.
TL;DR
A central theme is emphasised: that generative AI delivers speed and breadth, while physics-based simulation supplies thermodynamic rigour, and that their integration, rather than either alone, defines the most promising path forward.
Abstract
Allostery offers a powerful route to regulate protein function and expands drug discovery beyond the orthosteric paradigm. By acting at sites distinct from the active site, allosteric modulators can achieve greater selectivity, reduce off-target effects, and overcome resistance. The discovery of the cryptic switch-II pocket of KRAS, which turned a long-"undruggable" oncoprotein into a clinically validated target, exemplifies this promise. Yet allosteric drug discovery is demanding: it requires not only identifying a suitable, often transient pocket, but also demonstrating that this pocket is functionally coupled to the active site, and then translating that mechanistic insight into design. This perspective surveys the computational strategies addressing each of these challenges in turn: sequence, structure, and machine-learning-based methods for locating allosteric and cryptic sites; network and dynamical analyses for mapping communication pathways; and enhanced-sampling and generative deep-learning approaches for rational modulator design. Throughout, we emphasise a central theme: that generative AI delivers speed and breadth, while physics-based simulation supplies thermodynamic rigour, and that their integration, rather than either alone, defines the most promising path forward. Together with experimental validation, these advances are rapidly expanding our ability to exploit allosteric regulation in therapeutics.