ArticleRSC advances2019
LSA: a local-weighted structural alignment tool for pharmaceutical virtual screening.
Article in RSC advances, 2019. 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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Who cites it
3 citing papers in PubMed.
- The changing scenario of drug discovery using AI to deep learning: Recent advancement, success stories, collaborations, and challenges.Molecular therapy. Nucleic acids · 2024Review
- Development of Antiepileptic Drugs throughout History: From Serendipity to Artificial Intelligence.Biomedicines · 2023Review
- Artificial intelligence to deep learning: machine intelligence approach for drug discovery.Molecular diversity · 2021Review
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Similar structures having similar activities is a dogma for identifying new functional molecules. However, it is not rare that a minor structural change can cause a significant activity change. Methods to measure the molecular similarity can be classified into two categories of overall three-dimensional shape based methods and local substructure based methods. The former states the relation between overall similarity and activity, and is represented by conventional similarity algorithms. The latter states the relation between local substructure and activity, and is represented by conventional substructure match algorithms. Practically, the similarity of two molecules with similar activity depends on the contributions from both overall similarity and local substructure match. We report a new tool termed as a local-weighted structural alignment (LSA) tool for pharmaceutical virtual screening, which computes the similarity of two molecular structures by considering the contributions of both overall similarity and local substructure match. LSA consists of three steps: (1) mapping a common substructure between two molecular topological structures; (2) superimposing two three-dimensional molecular structures with substructure focus; (3) computing the similarity score based on superimposing. LSA has been validated with 102 testing compound libraries from DUD-E collection with the average AUC (the area under a receiver-operating characteristic curve) value of 0.82 and an average EF
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Registered trials
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