CSatDTA: Prediction of Drug–Target Binding Affinity Using Convolution Model with Self-Attention

Ashutosh Ghimire, Hilal Tayara, Zhenyu Xuan, Kil To Chong

Research output: Contribution to journalArticlepeer-review

Abstract

Drug discovery, which aids to identify potential novel treatments, entails a broad range of fields of science, including chemistry, pharmacology, and biology. In the early stages of drug development, predicting drug–target affinity is crucial. The proposed model, the prediction of drug–target affinity using a convolution model with self-attention (CSatDTA), applies convolution-based self-attention mechanisms to the molecular drug and target sequences to predict drug–target affinity (DTA) effectively, unlike previous convolution methods, which exhibit significant limitations related to this aspect. The convolutional neural network (CNN) only works on a particular region of information, excluding comprehensive details. Self-attention, on the other hand, is a relatively recent technique for capturing long-range interactions that has been used primarily in sequence modeling tasks. The results of comparative experiments show that CSatDTA surpasses previous sequence-based or other approaches and has outstanding retention abilities.

Original languageEnglish (US)
Article number8453
JournalInternational journal of molecular sciences
Volume23
Issue number15
DOIs
StatePublished - Aug 2022
Externally publishedYes

Keywords

  • artificial intelligence
  • attention
  • binding affinity
  • convolution neural network
  • deep learning
  • drug discovery and development
  • drug–target interaction
  • ligands
  • pharmacometrics
  • proteins

ASJC Scopus subject areas

  • Catalysis
  • Molecular Biology
  • Spectroscopy
  • Computer Science Applications
  • Physical and Theoretical Chemistry
  • Organic Chemistry
  • Inorganic Chemistry

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