Pocket representations play a vital role in various biomedical applications,
such as druggability estimation, ligand affinity prediction, and de novo drug
design. While existing geometric features and pretrained representations have
demonstrated promising results, they usually treat pockets independent of
ligands, neglecting the fundamental interactions between them. However, the
limited pocket-ligand complex structures available in the PDB database (less
than 100 thousand non-redundant pairs) hampers large-scale pretraining
endeavors for interaction modeling. To address this constraint, we propose a
novel pocket pretraining approach that leverages knowledge from high-resolution
atomic protein structures, assisted by highly effective pretrained small
molecule representations. By segmenting protein structures into drug-like
fragments and their corresponding pockets, we obtain a reasonable simulation of
ligand-receptor interactions, resulting in the generation of over 5 million
complexes. Subsequently, the pocket encoder is trained in a contrastive manner
to align with the representation of pseudo-ligand furnished by some pretrained
small molecule encoders. Our method, named ProFSA, achieves state-of-the-art
performance across various tasks, including pocket druggability prediction,
pocket matching, and ligand binding affinity prediction. Notably, ProFSA
surpasses other pretraining methods by a substantial margin. Moreover, our work
opens up a new avenue for mitigating the scarcity of protein-ligand complex
data through the utilization of high-quality and diverse protein structure
databases.

通过在高分辨率原子蛋白质结构中分割药物样片段和相应的口袋，并与一些预训练的小分子编码器生成的伪配体表示对齐，我们提出了一种新颖的口袋预训练方法 ProFSA，该方法在口袋可药性预测、口袋匹配和配体结合亲和力预测等多个任务中取得了最先进的性能，同时通过利用高质量和多样性的蛋白质结构数据库，我们的研究为缓解蛋白质 - 配体复合物数据的稀缺性开辟了新途径。