Recently, a noticeable trend has emerged in developing pre-trained foundation
models in the domains of CV and NLP. However, for molecular pre-training, there
lacks a universal model capable of effectively applying to various categories
of molecular tasks, since existing prevalent pre-training methods exhibit
effectiveness for specific types of downstream tasks. Furthermore, the lack of
profound understanding of existing pre-training methods, including 2D graph
masking, 2D-3D contrastive learning, and 3D denoising, hampers the advancement
of molecular foundation models. In this work, we provide a unified
comprehension of existing pre-training methods through the lens of contrastive
learning. Thus their distinctions lie in clustering different views of
molecules, which is shown beneficial to specific downstream tasks. To achieve a
complete and general-purpose molecular representation, we propose a novel
pre-training framework, named UniCorn, that inherits the merits of the three
methods, depicting molecular views in three different levels. SOTA performance
across quantum, physicochemical, and biological tasks, along with comprehensive
ablation study, validate the universality and effectiveness of UniCorn.

通过对比学习，我们提出了一种名为 UniCorn 的新型预训练框架，具备对分子进行三个不同层次的视图描绘，从而实现了完整且通用的分子表示，并在量子、物理化学和生物学任务中取得了最佳性能，验证了 UniCorn 的普适性和有效性。

UniCorn: 多视图分子表示学习的统一对比学习方法

UniCorn: A Unified Contrastive Learning Approach for Multi-view  Molecular Representation Learning

The rapid evolution of artificial intelligence in drug discovery encounters
challenges with generalization and extensive training, yet Large Language
Models (LLMs) offer promise in reshaping interactions with complex molecular
data. Our novel contribution, InstructMol, a multi-modal LLM, effectively
aligns molecular structures with natural language via an instruction-tuning
approach, utilizing a two-stage training strategy that adeptly combines limited
domain-specific data with molecular and textual information. InstructMol
showcases substantial performance improvements in drug discovery-related
molecular tasks, surpassing leading LLMs and significantly reducing the gap
with specialized models, thereby establishing a robust foundation for a
versatile and dependable drug discovery assistant.

通过一种多模态的大型语言模型 InstructMol，将分子结构与自然语言有效地对齐，通过一种指令调整方法，利用有限的领域特定数据与分子和文本信息相结合的两阶段训练策略，展示了在药物发现相关的分子任务上的显著性能改进，超过了领先的大型语言模型，并显著缩小了与专业模型之间的差距，从而为实现一个多才多艺、可靠的药物发现助手奠定了坚实的基础。

InstructMol: 用于药物发现中构建多模态集成的多功能可靠分子助手

InstructMol: Multi-Modal Integration for Building a Versatile and  Reliable Molecular Assistant in Drug Discovery

The ability of graph neural networks (GNNs) to count certain graph
substructures, especially cycles, is important for the success of GNNs on a
wide range of tasks. It has been recently used as a popular metric for
evaluating the expressive power of GNNs. Many of the proposed GNN models with
provable cycle counting power are based on subgraph GNNs, i.e., extracting a
bag of subgraphs from the input graph, generating representations for each
subgraph, and using them to augment the representation of the input graph.
However, those methods require heavy preprocessing, and suffer from high time
and memory costs. In this paper, we overcome the aforementioned limitations of
subgraph GNNs by proposing a novel class of GNNs -- $d$-Distance-Restricted
FWL(2) GNNs, or $d$-DRFWL(2) GNNs. $d$-DRFWL(2) GNNs use node pairs whose
mutual distances are at most $d$ as the units for message passing to balance
the expressive power and complexity. By performing message passing among
distance-restricted node pairs in the original graph, $d$-DRFWL(2) GNNs avoid
the expensive subgraph extraction operations in subgraph GNNs, making both the
time and space complexity lower. We theoretically show that the discriminative
power of $d$-DRFWL(2) GNNs strictly increases as $d$ increases. More
importantly, $d$-DRFWL(2) GNNs have provably strong cycle counting power even
with $d=2$: they can count all 3, 4, 5, 6-cycles. Since 6-cycles (e.g., benzene
rings) are ubiquitous in organic molecules, being able to detect and count them
is crucial for achieving robust and generalizable performance on molecular
tasks. Experiments on both synthetic datasets and molecular datasets verify our
theory. To the best of our knowledge, our model is the most efficient GNN model
to date (both theoretically and empirically) that can count up to 6-cycles.

我们提出了一种新的 GNN 模型，$d$-DRFWL (2) GNNs，通过在原始图中对限制距离的节点对进行信息传递，避免了子图 GNN 模型中昂贵的子图提取操作，从而降低了时间和空间复杂度。实验证实了我们的理论，我们的模型在计算 3、4、5、6 环等循环结构时是迄今为止效率最高的 GNN 模型。