MDeePred: novel multi-channel protein featurization for deep learning-based binding affinity prediction in drug discovery

Rifaioglu A. S., Atalay R. C., KAHRAMAN D. C., DOĞAN T., Martin M., Atalay V.

BIOINFORMATICS, cilt.37, sa.5, ss.693-704, 2021 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 37 Sayı: 5
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1093/bioinformatics/btaa858
  • Dergi Adı: BIOINFORMATICS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.693-704
  • Hacettepe Üniversitesi Adresli: Evet

Özet

Motivation: Identification of interactions between bioactive small molecules and target proteins is crucial for novel drug discovery, drug repurposing and uncovering off-target effects. Due to the tremendous size of the chemical space, experimental bioactivity screening efforts require the aid of computational approaches. Although deep learning models have been successful in predicting bioactive compounds, effective and comprehensive featurization of proteins, to be given as input to deep neural networks, remains a challenge.