Interaction prediction in structure-based virtual screening using deep learning

Adam Gonczarek*, Jakub M. Tomczak, Szymon Zaręba, Joanna Kaczmar, Piotr Dąbrowski, Michał J. Walczak

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review


We introduce a deep learning architecture for structure-based virtual screening that generates fixed-sized fingerprints of proteins and small molecules by applying learnable atom convolution and softmax operations to each molecule separately. These fingerprints are further non-linearly transformed, their inner product is calculated and used to predict the binding potential. Moreover, we show that widely used benchmark datasets may be insufficient for testing structure-based virtual screening methods that utilize machine learning. Therefore, we introduce a new benchmark dataset, which we constructed based on DUD-E, MUV and PDBBind databases.

Original languageEnglish
Pages (from-to)253-258
Number of pages6
JournalComputers in Biology and Medicine
Publication statusPublished - 1 Sept 2018
Externally publishedYes


  • Deep learning
  • DUD-E
  • Graph convolution
  • MUV
  • Neural fingerprint
  • PDBBind
  • Virtual screening


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