ArticleJournal of chemical theory and computation2024
Kartograf: A Geometrically Accurate Atom Mapper for Hybrid-Topology Relative Free Energy Calculations.
Article in Journal of chemical theory and computation, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Training a force field for proteins and small molecules from scratch.Chemical science · 2026Article
- Large-Scale Collaborative Assessment of Binding Free Energy Calculations for Drug Discovery Using OpenFE.Journal of chemical information and modeling · 2026Article
- Developing and Benchmarking Sage 2.3.0 with the AshGC Neural Network Charge Model.Journal of chemical theory and computation · 2026Article
- Doing More with Less: Accurate and Scalable Ligand Free Energy Calculations by Focusing on the Binding Site.Journal of chemical information and modeling · 2026Article
- The Open Molecular Software Foundation (OMSF) and the Growing Role of Open Source Software in Molecular Modeling.Journal of chemical information and modeling · 2026Review
- Moldrug algorithm for an automated ligand binding site exploration by 3D aware molecular enumerations.Journal of cheminformatics · 2025Article
- IMERGE-FEP: Improving Relative Free Energy Calculation Convergence with Chemical Intermediates.The journal of physical chemistry. B · 2025Article
- Multiple Topology Replica Exchange of Expanded Ensembles for Multidimensional Alchemical Calculations.Journal of chemical theory and computation · 2025Article
- Multistate Method to Efficiently Account for Tautomerism and Protonation in Alchemical Free-Energy Calculations.Journal of chemical theory and computation · 2024Article
- Kartograf: A Geometrically Accurate Atom Mapper for Hybrid-Topology Relative Free Energy Calculations.Journal of chemical theory and computation · 2024Article
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Authors and funding
7 authors.
Funding
Abstract
Relative binding free energy (RBFE) calculations have emerged as a powerful tool that supports ligand optimization in drug discovery. Despite many successes, the use of RBFEs can often be limited by automation problems, in particular, the setup of such calculations. Atom mapping algorithms are an essential component in setting up automatic large-scale hybrid-topology RBFE calculation campaigns. Traditional algorithms typically employ a 2D subgraph isomorphism solver (SIS) in order to estimate the maximum common substructure. SIS-based approaches can be limited by time-intensive operations and issues with capturing geometry-linked chemical properties, potentially leading to suboptimal solutions. To overcome these limitations, we have developed Kartograf, a geometric-graph-based algorithm that uses primarily the 3D coordinates of atoms to find a mapping between two ligands. In free energy approaches, the ligand conformations are usually derived from docking or other previous modeling approaches, giving the coordinates a certain importance. By considering the spatial relationships between atoms related to the molecule coordinates, our algorithm bypasses the computationally complex subgraph matching of SIS-based approaches and reduces the problem to a much simpler bipartite graph matching problem. Moreover, Kartograf effectively circumvents typical mapping issues induced by molecule symmetry and stereoisomerism, making it a more robust approach for atom mapping from a geometric perspective. To validate our method, we calculated mappings with our novel approach using a diverse set of small molecules and used the mappings in relative hydration and binding free energy calculations. The comparison with two SIS-based algorithms showed that Kartograf offers a fast alternative approach. The code for Kartograf is freely available on GitHub (https://github.com/OpenFreeEnergy/kartograf). While developed for the OpenFE ecosystem, Kartograf can also be utilized as a standalone Python package.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.