MulinforCPI: enhancing precision of compound–protein interaction prediction through novel perspectives on multi-level information integration

Ngoc Quang Nguyen, Sejeong Park, Mogan Gim, Jaewoo Kang

Research output: Contribution to journalArticlepeer-review

Abstract

Forecasting the interaction between compounds and proteins is crucial for discovering new drugs. However, previous sequence-based studies have not utilized three-dimensional (3D) information on compounds and proteins, such as atom coordinates and distance matrices, to predict binding affinity. Furthermore, numerous widely adopted computational techniques have relied on sequences of amino acid characters for protein representations. This approach may constrain the model’s ability to capture meaningful biochemical features, impeding a more comprehensive understanding of the underlying proteins. Here, we propose a two-step deep learning strategy named MulinforCPI that incorporates transfer learning techniques with multi-level resolution features to overcome these limitations. Our approach leverages 3D information from both proteins and compounds and acquires a profound understanding of the atomic-level features of proteins. Besides, our research highlights the divide between first-principle and data-driven methods, offering new research prospects for compound–protein interaction tasks. We applied the proposed method to six datasets: Davis, Metz, KIBA, CASF-2016, DUDE and BindingDB, to evaluate the effectiveness of our approach.

Original languageEnglish
Article numberbbad484
JournalBriefings in Bioinformatics
Volume25
Issue number1
DOIs
Publication statusPublished - 2024 Jan 1

Bibliographical note

Publisher Copyright:
© 2024 Oxford University Press. All rights reserved.

Keywords

  • 3D geometric information
  • compound–protein interaction
  • cross-attention
  • multi-level information
  • transfer learning

ASJC Scopus subject areas

  • Information Systems
  • Molecular Biology

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