  Pretraining on a large number of unlabeled 3D molecules has showcased
superiority in various scientific applications. However, prior efforts
typically focus on pretraining models on a specific domain, either proteins or
small molecules, missing the opportunity to leverage the cross-domain
knowledge. To mitigate this gap, we introduce Equivariant Pretrained
Transformer (EPT), a novel pretraining framework designed to harmonize the
geometric learning of small molecules and proteins. To be specific, EPT unifies
the geometric modeling of multi-domain molecules via the block-enhanced
representation that can attend a broader context of each atom. Upon transformer
framework, EPT is further enhanced with E(3) equivariance to facilitate the
accurate representation of 3D structures. Another key innovation of EPT is its
block-level pretraining task, which allows for joint pretraining on datasets
comprising both small molecules and proteins. Experimental evaluations on a
diverse group of benchmarks, including ligand binding affinity prediction,
molecular property prediction, and protein property prediction, show that EPT
significantly outperforms previous SOTA methods for affinity prediction, and
achieves the best or comparable performance with existing domain-specific
pretraining models for other tasks.
