ArticleJournal of cheminformatics2022
Visualizing chemical space networks with RDKit and NetworkX.
Article in Journal of cheminformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed, 87 citations in OpenAlex.
- An integrated machine learning and chemical space network approach for the design of potent epigenetic HDAC6 inhibitors for targeting neurological disorders.Molecular diversity · 2026Article
- Efficient visualization of chemical space.Chemical science · 2026Article
- MSCA-PLA: multi-scale cross-attention with differentiable pooling for protein-ligand binding affinity prediction.Molecular diversity · 2026Article
- Chemical space visualization at scale: a survey of end-to-end pipelines and dataset-size archetypes.Journal of cheminformatics · 2026Review
- AI-driven fungicide design: From target identification to field application.Plant communications · 2026Review
- GRU-based de novo design and in-silico prioritization of EZH2 inhibitors.Molecular diversity · 2026Article
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- Accelerated discovery of new organic photovoltaic dyes for OPVs using light absorbance as the primary screening criterionRSC advances · 2025Article
- Synergistic approach utilizing bioinformatics, machine learning, and traditional screening for the identification of novel CSK inhibitors targeting hepatocellular carcinoma.Journal of computer-aided molecular design · 2025Article
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- Python tools for structural tasks in chemistry.Molecular diversity · 2025Review
- PPARγ modulator predictor (PGMP_v1): chemical space exploration and computational insights for enhanced type 2 diabetes mellitus management.Molecular diversity · 2025Article
- First report on analysis of chemical space, scaffold diversity, critical structural features of HDAC11 inhibitors.Molecular diversity · 2025Article
- Molecular Mediated Angiogenesis and Vasculogenesis Networks.International journal of molecular sciences · 2025Article
- Machine learning-based bioactivity prediction of porphyrin derivatives: molecular descriptors, clustering, and model evaluation.Photochemical & photobiological sciences : Official journal of the European Photochemistry Association and the European Society for Photobiology · 2025Article
- Scaffold and SAR studies on c-MET inhibitors using machine learning approaches.Journal of pharmaceutical analysis · 2025Article
- Machine Learning-Driven Consensus Modeling for Activity Ranking and Chemical Landscape Analysis of HIV-1 Inhibitors.Pharmaceuticals (Basel, Switzerland) · 2025Article
- Labels as a feature: Network homophily for systematically annotating human GPCR drug-target interactions.Nature communications · 2025Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This article demonstrates how to create Chemical Space Networks (CSNs) using a Python RDKit and NetworkX workflow. CSNs are a type of network visualization that depict compounds as nodes connected by edges, defined as a pairwise relationship such as a 2D fingerprint similarity value. A step by step approach is presented for creating two different CSNs in this manuscript, one based on RDKit 2D fingerprint Tanimoto similarity values, and another based on maximum common substructure similarity values. Several different CSN visualization features are included in the tutorial including methods to represent nodes with color based on bioactivity attribute value, edges with different line styles based on similarity value, as well as replacing the circle nodes with 2D structure depictions. Finally, some common network property and analysis calculations are presented including the clustering coefficient, degree assortativity, and modularity. All code is provided in the form of Jupyter Notebooks and is available on GitHub with a permissive BSD-3 open-source license: https://github.com/vfscalfani/CSN_tutorial.
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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.