AIMSim: An accessible cheminformatics platform for similarity operations on chemicals datasets

The recent advances in deep learning, generative modeling, and statistical learning have ushered in a renewed interest in traditional cheminformatics tools and methods. Quantifying molecular similarity is essential in molecular generative modeling, exploratory molecular synthesis campaigns, and drug-discovery applications to assess how new molecules differ from existing ones. Most tools target advanced users and lack general implementations accessible to the larger community. In this work, we introduce Artificial Intelligence Molecular Similarity (AIMSim), an accessible cheminformatics platform for performing similarity operations on collections of molecules called molecular datasets. AIMSim provides a unified platform to perform similarity-based tasks on molecular datasets, such as diversity quantification, outlier and novelty analysis, clustering, dimensionality reduction, and inter-molecular comparisons. AIMSim implements all major binary similarity metrics and molecular fingerprints and is provided as a Python package that includes support for command-line use as well as a Graphical User Interface for code-free utilization with fully interactive plots.近年来，深度学习、生成式建模与统计学习领域的诸多进展，重新引发了学界对传统化学信息学工具与方法的广泛关注。量化分子相似度在分子生成建模、探索性分子合成研究项目以及药物发现应用中至关重要，可用于评估新分子与现有分子之间的差异程度。当前多数工具仅面向高级用户开发，缺乏可供广大科研群体便捷使用的通用实现方案。本研究推出人工智能分子相似度（Artificial Intelligence Molecular Similarity，AIMSim）平台，这是一款易用的化学信息学工具，能够对被称为分子数据集的分子集合执行相似度计算操作。AIMSim提供了统一化平台，可针对分子数据集开展各类基于相似度的任务，包括多样性量化、异常值与新颖性分析、聚类、降维以及分子间比对。AIMSim集成了所有主流二元相似度指标与分子指纹，并以Python软件包的形式发布，支持命令行调用，同时配备了无需编写代码即可使用的图形用户界面，且内置全交互式绘图功能。