Prediction of pharmacological activities from chemical structures with graph convolutional neural networks

• Sakai, Miyuki

• Nagayasu, Kazuki

• Shibui, Norihiro

• Andoh, Chihiro

• Takayama, Kaito

• Shirakawa, Hisashi

• Kaneko, Shuji

Abstract

Many therapeutic drugs are compounds that can be represented by simple chemical structures, which contain important determinants of affinity at the site of action. Recently, graph convolutional neural network (GCN) models have exhibited excellent results in classifying the activity of such compounds. For models that make quantitative predictions of activity, more complex information has been utilized, such as the three-dimensional structures of compounds and the amino acid sequences of their respective target proteins. As another approach, we hypothesized that if sufficient experimental data were available and there were enough nodes in hidden layers, a simple compound representation would quantitatively predict activity with satisfactory accuracy. In this study, we report that GCN models constructed solely from the two-dimensional structural information of compounds demonstrated a high degree of activity predictability against 127 diverse targets from the ChEMBL database. Using the information entropy as a metric, we also show that the structural diversity had less effect on the prediction performance. Finally, we report that virtual screening using the constructed model identified a new serotonin transporter inhibitor with activity comparable to that of a marketed drug in vitro and exhibited antidepressant effects in behavioural studies.

化合物の薬理作用を予測する技術を開発 --薬理作用ビッグデータを用いて--. 京都大学プレスリリース. 2021-01-13.

Journal

• Scientific Reports

  Scientific Reports 11 525-, 2021-01-12

  Springer Nature

Keywords

• Data mining
• Drug safety
• Drug screening
• High-throughput screening
• Machine learning
• Neuroscience
• Pharmaceutics
• Pharmacology
• Toxicology
• Transporters in the nervous system
• Virtual drug screening

Details

• CRID: 1050568772234669824
• NII Article ID: 120006951708
• ISSN: 20452322
• DOI: 10.1038/s41598-020-80113-7
• HANDLE: 2433/260969
• Web Site: https://www.nature.com/articles/s41598-020-80113-7.pdf
• Web Site: https://www.nature.com/articles/s41598-020-80113-7
• Text Lang: en
• Article Type: journal article
• Data Source:
  • IRDB
  • Crossref
  • CiNii Articles
  • KAKEN