Artificial Intelligence predicts drug properties by encoding drug molecules,
aiding in the rapid screening of candidates. Different molecular
representations, such as SMILES and molecule graphs, contain complementary
information for molecular encoding. Thus exploiting complementary information
from different molecular representations is one of the research priorities in
molecular encoding. Most existing methods for combining molecular
multi-modalities only use molecular-level information, making it hard to encode
intra-molecular alignment information between different modalities. To address
this issue, we propose a multi-granularity fusion method that is MolFusion. The
proposed MolFusion consists of two key components: (1) MolSim, a
molecular-level encoding component that achieves molecular-level alignment
between different molecular representations. and (2) AtomAlign, an atomic-level
encoding component that achieves atomic-level alignment between different
molecular representations. Experimental results show that MolFusion effectively
utilizes complementary multimodal information, leading to significant
improvements in performance across various classification and regression tasks.

人工智能通过编码药物分子来预测药物特性，帮助快速筛选候选药物。利用不同来源的分子表示，如 SMILES 和分子图，可以提供互补信息进行分子编码。我们提出了一种多粒度融合方法 MolFusion，该方法利用分子级和原子级编码实现不同分子表示之间的相互对齐，实验结果表明 MolFusion 有效地利用了多模态的互补信息，在不同分类和回归任务中显著提高了性能。

MolFusion: 分子表示的多模态融合学习 -- 基于多粒度视图

MolFusion: Multimodal Fusion Learning for Molecular Representations via  Multi-granularity Views

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.

我们提出了一种新的大规模分子模型，名为 AdaMR，采用可调节的分子编码器，学习分子的原子级和亚结构级表示，并通过分子规范化任务进行预训练，从而在多个下游任务中改善效果，如模型属性预测和分子生成。