Adjustable Molecular Representation for Unified Pre-training Strategy
Yan Ding Hao Cheng Zeliang Ye Ruyi Feng Zhongze Gu
We propose a new large-scale molecular model, named AdaMR, which stands for
Adjustable Molecular Representation for Unified Pre-training Strategy. Unlike
recent large-scale molecular models that use a single molecular encoding, AdaMR
employs a granularity-adjustable molecular encoder, learning molecular
representations at both the atomic and substructure levels. For the
pre-training process, we designed a task for molecular canonicalization, which
involves transforming ltiple generic molecular representations into canonical
representations. By adjusting the granularity of molecular encoding, the
trained model can improve the effects on multiple downstream tasks, such as
model attribute prediction and molecule generation. Substructure-level
molecular representation retains information of specific atom groups or
arrangements that determine chemical properties and have similar functions,
which is beneficial for tasks like property prediction. Meanwhile, atomic-level
representation, combined with generative molecular canonicalization
pre-training tasks, enhances the validity, novelty, and uniqueness in
generative tasks. These features of AdaMR demonstrate its strong performance in
numerous downstream tasks. We use different molecular properties prediction
tasks on six different datasets on MoleculeNet and two generative tasks on
ZINC250K dataset to evaluate our proposed molecular encoding and pre-training
methods, and obtain state-of-the-art (SOTA) results on five of these tasks.