ArticleMolecules (Basel, Switzerland)2018
Substructural Connectivity Fingerprint and Extreme Entropy Machines-A New Method of Compound Representation and Analysis.
Article in Molecules (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Group graph: a molecular graph representation with enhanced performance, efficiency and interpretability.Journal of cheminformatics · 2024Article
- Exploring the Potential of Spherical Harmonics and PCVM for Compounds Activity Prediction.International journal of molecular sciences · 2019Article
- Fingerprint-Based Machine Learning Approach to Identify Potent and Selective 5-HTMolecules (Basel, Switzerland) · 2018Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Key-based substructural fingerprints are an important element of computer-aided drug design techniques. The usefulness of the fingerprints in filtering compound databases is invaluable, as they allow for the quick rejection of molecules with a low probability of being active. However, this method is flawed, as it does not consider the connections between substructures. After changing the connections between particular chemical moieties, the fingerprint representation of the compound remains the same, which leads to difficulties in distinguishing between active and inactive compounds. In this study, we present a new method of compound representation-substructural connectivity fingerprints (SCFP), providing information not only about the presence of particular substructures in the molecule but also additional data on substructure connections. Such representation was analyzed by the recently developed methodology-extreme entropy machines (EEM). The SCFP can be a valuable addition to virtual screening tools, as it represents compound structure with greater detail and more specificity, allowing for more accurate classification.
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