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 fully functional Graphical User Interface for code-free utilization with fully interactive plots.

Version notes

This revised version contains changes requested during peer review and a redesign of the AIMSim Graphical User Interface.


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Supplementary material

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Supporting Information for AIMSim: An Accessible Cheminformatics Platform for Similarity Operations on Chemicals Datasets
Tabulated Similarity Measures, Graphical User Interface Walkthrough, Cluster Analysis of Solvents in Use Case, Speedup and Efficiency Tables, Statement of Availability of Source Code

Supplementary weblinks

AIMSim Documentation
Comprehensive documentation for AIMSim, including installation tutorials, usage tutorials, tabulated similarity metrics, and module structure tree.