ArticleJournal of chemical information and modeling2019
Ligand-Based Virtual Screening Using Graph Edit Distance as Molecular Similarity Measure.
Article in Journal of chemical information and modeling, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- GESim: ultrafast graph-based molecular similarity calculation via von Neumann graph entropy.Journal of cheminformatics · 2025Article
- Decoding Drug Discovery: Exploring A-to-Z In Silico Methods for Beginners.Applied biochemistry and biotechnology · 2025Review
- Unlocking the Potential of High-Quality Dopamine Transporter Pharmacological Data: Advancing Robust Machine Learning-Based QSAR Modeling.Advances in neurobiology · 2025Review
- DrugSynthMC: An Atom-Based Generation of Drug-like Molecules with Monte Carlo Search.Journal of chemical information and modeling · 2024Article
- ATENA: A Web-Based Tool for Modelling Metal Oxide Nanoparticles Based on NanoFingerprint Quantitative Structure-Activity Relationships.Molecules (Basel, Switzerland) · 2024Article
- Enhancing property and activity prediction and interpretation using multiple molecular graph representations with MMGX.Communications chemistry · 2024Article
- Expanding Training Data for Structure-Based Receptor-Ligand Binding Affinity Regression through Imputation of Missing Labels.ACS omega · 2023Article
- Employing Molecular Conformations for Ligand-Based Virtual Screening with Equivariant Graph Neural Network and Deep Multiple Instance Learning.Molecules (Basel, Switzerland) · 2023Article
- Drug Potency Prediction of SARS-CoV-2 Main Protease Inhibitors Based on a Graph Generative Model.International journal of molecular sciences · 2023Article
- Article
- Application of deep metric learning to molecular graph similarity.Journal of cheminformatics · 2022Article
- Selecting molecules with diverse structures and properties by maximizing submodular functions of descriptors learned with graph neural networks.Scientific reports · 2022Article
- Article
- Ligand-Based Virtual Screening Based on the Graph Edit Distance.International journal of molecular sciences · 2021Article
- Applications of Virtual Screening in Bioprospecting: Facts, Shifts, and Perspectives to Explore the Chemo-Structural Diversity of Natural Products.Frontiers in chemistry · 2021Review
- Learning the Edit Costs of Graph Edit Distance Applied to Ligand-Based Virtual Screening.Current topics in medicinal chemistry · 2020Article
Corrections and comments
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Extended reduced graphs provide summary representations of chemical structures using pharmacophore-type node descriptions to encode the relevant molecular properties. Commonly used similarity measures using reduced graphs convert these graphs into 2D vectors like fingerprints, before chemical comparisons are made. This study investigates the effectiveness of a graph-only driven molecular comparison by using extended reduced graphs along with graph edit distance methods for molecular similarity calculation as a tool for ligand-based virtual screening applications, which estimate the bioactivity of a chemical on the basis of the bioactivity of similar compounds. The results proved to be very stable and the graph editing distance method performed better than other methods previously used on reduced graphs. This is exemplified with six publicly available data sets: DUD-E, MUV, GLL&GDD, CAPST, NRLiSt BDB, and ULS-UDS. The screening and statistical tools available on the ligand-based virtual screening benchmarking platform and the RDKit were also used. In the experiments, our method performed better than other molecular similarity methods which use array representations in most cases. Overall, it is shown that extended reduced graphs along with graph edit distance is a combination of methods that has numerous applications and can identify bioactivity similarities in a structurally diverse group of molecules.
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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.