ArticleBioinformatics advances2023
Multi-shelled ECIF: improved extended connectivity interaction features for accurate binding affinity prediction.
Article in Bioinformatics advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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
2 citing papers in PubMed.
- Critical Artifacts Improve Reproducibility of Protein-Ligand Binding Affinity Prediction Models on CASF-2016.Journal of chemical information and modeling · 2026Article
- From Static to Dynamic Structures: Improving Binding Affinity Prediction with Graph-Based Deep Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
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Authors and funding
2 authors.
Funding
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Abstract
Motivation: Extended connectivity interaction features (ECIF) is a method developed to predict protein-ligand binding affinity, allowing for detailed atomic representation. It performed very well in terms of Comparative Assessment of Scoring Functions 2016 (CASF-2016) scoring power. However, ECIF has the limitation of not being able to adequately account for interatomic distances. Results: To investigate what kind of distance representation is effective for P-L binding affinity prediction, we have developed two algorithms that improved ECIF's feature extraction method to take distance into account. One is multi-shelled ECIF, which takes into account the distance between atoms by dividing the distance between atoms into multiple layers. The other is weighted ECIF, which weights the importance of interactions according to the distance between atoms. A comparison of these two methods shows that multi-shelled ECIF outperforms weighted ECIF and the original ECIF, achieving a CASF-2016 scoring power Pearson correlation coefficient of 0.877. Availability and implementation: All the codes and data are available on GitHub (https://github.com/koji11235/MSECIFv2).
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Registered trials
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