Three-Dimensional Analysis of Binding Sites for Predicting Binding Affinities in Drug Design

Erdas-Cicek O., Atac A. O., Gurkan-Alp A. S., Buyukbingol E., ALPASLAN F. N.

JOURNAL OF CHEMICAL INFORMATION AND MODELING, cilt.59, sa.11, ss.4654-4662, 2019 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 59 Sayı: 11
  • Basım Tarihi: 2019
  • Doi Numarası: 10.1021/acs.jcim.9b00206
  • Dergi Adı: JOURNAL OF CHEMICAL INFORMATION AND MODELING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.4654-4662
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

Understanding the interaction between drug molecules and proteins is one of the main challenges in drug design. Several tools have been developed recently to decrease the complexity of the process. Artificial intelligence and machine learning methods offer promising results in predicting the binding affinities. It becomes possible to do accurate predictions by using the known protein-ligand interactions. In this study, the electrostatic potential values extracted from 3-dimensional grid cubes of the drug-protein binding sites are used for predicting binding affinities of related complexes. A new algorithm with a dynamic feature selection method was implemented, which is derived from Compressed Images For Affinity Prediction (CIFAP) study, to predict binding affinities of Checkpoint Kinase 1 and Caspase 3 inhibitors.