2026-07-22T06:23:57Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:25885 2026-06-23T13:00:13Z ddc:004 open_access

Mapping Chemical Space: Topological Data Analysis of Chemical Latent Space with Mapper Meduri, Dhruv Hu, Chuan-Shen Shen, Cong Xia, Kelin Wang, Bei Practice of computational topology topological data analysis applications in chemistry mapper algorithm high-dimensional data analysis chemical spaces geometric deep learning latent space geometry The vast chemical space, encompassing virtually innumerable molecules and materials, presents both immense opportunities and significant challenges. The design and discovery of novel drugs and functional materials may be viewed as a search within this space; however, the sheer scale of potential candidates renders exhaustive exploration infeasible. To address this, we introduce Chemical Mapper, a framework that integrates topological data analysis with deep learning to enable the visual exploration and analysis of chemical latent spaces. At its core, Chemical Mapper employs mapper, a widely used tool in topological data analysis, to investigate the organizational principles of chemical latent spaces defined by molecular representations learned by geometric deep learning models. In doing so, Chemical Mapper not only highlights groups of molecular representations but also uncovers the relationships among them through linkages and branching structures. Our results show that Chemical Mapper reveals intrinsic patterns associated with molecular scaffolds, functional groups, and chemical properties, as well as the structural and functional evolutions of the molecules. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Dhruv Meduri and Chuan-Shen Hu and Cong Shen and Kelin Xia and Bei Wang 2026 Is Part Of LIPIcs, Volume 367, 42nd International Symposium on Computational Geometry (SoCG 2026) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.SoCG.2026.78 urn:nbn:de:0030-drops-258854 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SoCG.2026.78 eng https://creativecommons.org/licenses/by/4.0/legalcode