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  • 1Department of Intelligence Science and Technology, Graduate School of Informatics, Kyoto University, Kyoto, Kyoto 606-8501, Japan.

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|November 6, 2023
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Summary
This summary is machine-generated.

We improved Extended Connectivity Interaction Features (ECIF) for predicting protein-ligand binding affinity by incorporating interatomic distances. Multi-shelled ECIF significantly enhances prediction accuracy, outperforming previous methods.

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Area of Science:

  • Computational chemistry
  • Structural biology
  • Drug discovery

Background:

  • Extended Connectivity Interaction Features (ECIF) is a method for predicting protein-ligand binding affinity.
  • ECIF provides detailed atomic representation and performed well in the Comparative Assessment of Scoring Functions 2016 (CASF-2016).
  • A key limitation of ECIF is its inability to adequately account for interatomic distances.

Purpose of the Study:

  • To investigate effective distance representations for protein-ligand (P-L) binding affinity prediction.
  • To develop improved ECIF algorithms that incorporate interatomic distance information.

Main Methods:

  • Developed two algorithms to enhance ECIF's feature extraction: multi-shelled ECIF and weighted ECIF.
  • Multi-shelled ECIF divides interatomic distances into multiple layers.
  • Weighted ECIF assigns importance to interactions based on interatomic distance.

Main Results:

  • Multi-shelled ECIF demonstrated superior performance compared to weighted ECIF and original ECIF.
  • Multi-shelled ECIF achieved a Pearson correlation coefficient of 0.877 in CASF-2016 scoring power.
  • The study highlights the importance of distance representation in P-L binding affinity prediction.

Conclusions:

  • Incorporating interatomic distances significantly improves ECIF's predictive power for protein-ligand binding affinity.
  • Multi-shelled ECIF is a promising advancement for accurate scoring function development.
  • The developed methods and code are publicly available for further research.