Many processes in biology and drug discovery involve various 3D interactions between different molecules, such as protein and protein, protein and small molecule, etc. Designing a generalist model to learn universal molecular interactions is valuable yet challenging, given that different molecules are usually represented in different granularity. In this paper, we first propose to universally represent a 3D molecule as a geometric graph of sets, in contrast to conventional single-level representations. Upon the proposed unified representation, we then propose a Generalist Equivariant Transformer (GET) to effectively capture both sparse block-level and dense atom-level interactions. To be specific, GET consists of a bilevel attention module, a feed-forward module and a layer normalization module, where, notably, each module is E(3) equivariant to meet the symmetry of 3D world. Extensive experiments on the prediction of protein-protein affinity, ligand binding affinity, and ligand efficacy prediction verify the effectiveness of our proposed method against existing methods, and reveal its potential to learn transferable knowledge across different domains and different tasks.

翻译：生物学和药物研发中的许多过程涉及不同分子间的多种三维相互作用，例如蛋白质-蛋白质相互作用、蛋白质-小分子相互作用等。设计一个通用模型来学习普适的分子交互具有重要价值但也极具挑战性，因为不同分子通常以不同粒度进行表示。本文首先提出以几何图集合的形式对三维分子进行通用表示，这与传统的单层级表示方法形成对比。基于该统一表示，我们进一步提出通用等变Transformer（GET），以有效捕获稀疏的块级交互与密集的原子级交互。具体而言，GET包含双层注意力模块、前馈模块和层归一化模块——值得注意的是，每个模块均具备E(3)等变性以符合三维世界的对称性。在蛋白质-蛋白质亲和力预测、配体结合亲和力预测以及配体功效预测任务上的大量实验验证了我们提出方法相较于现有方法的有效性，并揭示了其在不同领域与不同任务间学习可迁移知识的潜力。