Beyond the Rigid Pocket: Protein Flexibility-informed Ligand Generation for Structure-Based Drug Design

Structure-based ligand generation commonly assumes a fixed target protein structure, overlooking conformational heterogeneity and the structural adjustments that accompany ligand binding. Modeling protein flexibility remains challenging: matched apo-holo structure pairs are scarce, and pocket-focused methods often omit structural context beyond the binding site. Here, we present FlexiDD (Flexibility-Informed Drug Design), a generative framework for de novo drug design that explicitly models full-protein conformational adaptation at atomic resolution during ligand generation. To address the scarcity of paired apo–holo data, FlexiDD introduces a transferable coordinate denoising strategy that uses coordinate perturbations to bring apo and holo conformations into overlapping noisy distributions. This enables training solely on holo complexes, while supporting both apo and holo inputs at inference. By modeling the full protein rather than isolated binding pockets, FlexiDD incorporates global structural context and constraints into local binding-site adaptation. Experiments on a leakage-audited benchmark demonstrate strong ligand generation performance, with favorable predicted binding affinities and higher PoseBusters validity rates across both holo and apo inputs. Under apo conditioning, FlexiDD achieves 91.6% PoseBusters validity, compared with less than 46.1% for the evaluated baselines that also account for target flexibility. These results demonstrate the potential of holo-only training and full-protein conformational modeling for ligand generation from apo structures, supporting FlexiDD as a practical approach to flexibility-informed structure-based drug design.