ArticleJournal of chemical information and modeling2019
Ligity: A Non-Superpositional, Knowledge-Based Approach to Virtual Screening.
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 4 papers.
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Who cites it
4 citing papers in PubMed.
- DrugRep: an automatic virtual screening server for drug repurposing.Acta pharmacologica Sinica · 2023Article
- Artificial intelligence to deep learning: machine intelligence approach for drug discovery.Molecular diversity · 2021Review
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5 authors.
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Abstract
We present Ligity, a hybrid ligand-structure-based, non-superpositional method for virtual screening of large databases of small molecules. Ligity uses the relative spatial distribution of pharmacophoric interaction points (PIPs) derived from the conformations of small molecules. These are compared with the PIPs derived from key interaction features found in protein-ligand complexes and are used to prioritize likely binders. We investigated the effect of generating PIPs using the single lowest energy conformer versus an ensemble of conformers for each screened ligand, using different bin sizes for the distance between two features, utilizing triangular sets of pharmacophoric features (3-PIPs) versus chiral tetrahedral sets (4-PIPs), fusing data for targets with multiple protein-ligand complex structures, and applying different similarity measures. Ligity was benchmarked using the Directory of Useful Decoys-Enhanced (DUD-E). Optimal results were obtained using the tetrahedral PIPs derived from an ensemble of bound ligand conformers and a bin size of 1.5 Å, which are used as the default settings for Ligity. The high-throughput screening mode of Ligity, using only the lowest-energy conformer of each ligand, was used for benchmarking against the whole of the DUD-E, and a more resource-intensive, "information-rich" mode of Ligity, using a conformational ensemble of each ligand, were used for a representative subset of 10 targets. Against the full DUD-E database, mean area under the receiver operating characteristic curve (AUC) values ranged from 0.44 to 0.99, while for the representative subset they ranged from 0.61 to 0.86. Data fusion further improved Ligity's performance, with mean AUC values ranging from 0.64 to 0.95. Ligity is very efficient compared to a protein-ligand docking method such as AutoDock Vina: if the time taken for the precalculation of Ligity descriptors is included in the comparason, then Ligity is about 20 times faster than docking. A direct comparison of the virtual screening steps shows Ligity to be over 5000 times faster. Ligity highly ranks the lowest-energy conformers of DUD-E actives, in a statistically significant manner, behavior that is not observed for DUD-E decoys. Thus, our results suggest that active compounds tend to bind in relatively low-energy conformations compared to decoys. This may be because actives-and thus their lowest-energy conformations-have been optimized for conformational complementarity with their cognate binding sites.
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