Artificial intelligence (AI) is significantly transforming scientific
research. Explainable AI methods, such as concept-based models (CMs), are
promising for driving new scientific discoveries because they make predictions
based on meaningful concepts and offer insights into the prediction process. In
molecular science, however, explainable CMs are not as common compared to
black-box models like Graph Neural Networks (GNNs), primarily due to their
requirement for predefined concepts and manual label for each instance, which
demand domain knowledge and can be labor-intensive. This paper introduces a
novel framework for Automated Molecular Concept (AutoMolCo) generation and
labeling. AutoMolCo leverages the knowledge in Large Language Models (LLMs) to
automatically generate predictive molecular concepts and label them for each
molecule. Such procedures are repeated through iterative interactions with LLMs
to refine concepts, enabling simple linear models on the refined concepts to
outperform GNNs and LLM in-context learning on several benchmarks. The whole
AutoMolCo framework is automated without any human knowledge inputs in either
concept generation, labeling, or refinement, thereby surpassing the limitations
of extant CMs while maintaining their explainability and allowing easy
intervention. Through systematic experiments on MoleculeNet and High-Throughput
Experimentation (HTE) datasets, we demonstrate that the AutoMolCo-induced
explainable CMs are beneficial and promising for molecular science research.

通过自动分子概念生成和标记的 AutoMolCo 框架，利用大型语言模型的知识自动产生预测性的分子概念，经过迭代与语言模型的交互进行概念细化，使得基于细化后的概念的简单线性模型在多个基准测试中的表现超过了 GNNs 和 LLM 上下文学习的表现，从而克服了现有模型的限制，同时保持其可解释性并方便介入，证明了 AutoMolCo 引导的可解释概念模型对于分子科学研究的益处和前景。

大型语言模型的自动分子概念生成和标记

Automated Molecular Concept Generation and Labeling with Large Language  Models

Recent progress of deep generative models in the vision and language domain
has stimulated significant interest in more structured data generation such as
molecules. However, beyond generating new random molecules, efficient
exploration and a comprehensive understanding of the vast chemical space are of
great importance to molecular science and applications in drug design and
materials discovery. In this paper, we propose a new framework, ChemFlow, to
traverse chemical space through navigating the latent space learned by molecule
generative models through flows. We introduce a dynamical system perspective
that formulates the problem as learning a vector field that transports the mass
of the molecular distribution to the region with desired molecular properties
or structure diversity. Under this framework, we unify previous approaches on
molecule latent space traversal and optimization and propose alternative
competing methods incorporating different physical priors. We validate the
efficacy of ChemFlow on molecule manipulation and single- and multi-objective
molecule optimization tasks under both supervised and unsupervised molecular
discovery settings. Codes and demos are publicly available on GitHub at
this https URL

通过化学流动机制在分子生成模型的潜在空间中导航，提出了一种新的框架 ChemFlow，以遍历化学空间并实现在药物设计和材料发现方面的分子科学应用。

用潜在流量导航化学空间

Navigating Chemical Space with Latent Flows

Artificial intelligence has demonstrated immense potential in scientific
research. Within molecular science, it is revolutionizing the traditional
computer-aided paradigm, ushering in a new era of deep learning. With recent
progress in multimodal learning and natural language processing, an emerging
trend has targeted at building multimodal frameworks to jointly model molecules
with textual domain knowledge. In this paper, we present the first systematic
survey on multimodal frameworks for molecules research. Specifically,we begin
with the development of molecular deep learning and point out the necessity to
involve textual modality. Next, we focus on recent advances in text-molecule
alignment methods, categorizing current models into two groups based on their
architectures and listing relevant pre-training tasks. Furthermore, we delves
into the utilization of large language models and prompting techniques for
molecular tasks and present significant applications in drug discovery.
Finally, we discuss the limitations in this field and highlight several
promising directions for future research.

通过综述多模态分子研究的框架，本文探讨了人工智能在分子科学中的应用，特别关注文本与分子之间的对齐方法以及使用大型语言模型和提示技术进行分子任务的重要应用，同时讨论了该领域的局限性和未来研究的几个有希望的方向。