title: Pred-binding: large-scale protein-ligand binding affinity prediction.
abstract: Drug target interactions (DTIs) are crucial in pharmacology and drug discovery. Presently, experimental determination of compound-protein interactions remains challenging because of funding investment and difficulties of purifying proteins. In this study, we proposed two in silico models based on support vector machine (SVM) and random forest (RF), using 1589 molecular descriptors and 1080 protein descriptors in 9948 ligand-protein pairs to predict DTIs that were quantified by Ki values. The cross-validation coefficient of determination of 0.6079 for SVM and 0.6267 for RF were obtained, respectively. In addition, the two-dimensional (2D) autocorrelation, topological charge indices and three-dimensional (3D)-MoRSE descriptors of compounds, the autocorrelation descriptors and the amphiphilic pseudo-amino acid composition of protein are found most important for Ki predictions. These models provide a new opportunity for the prediction of ligand-receptor interactions that will facilitate the target discovery and toxicity evaluation in drug development.
authors.affiliation: a Bioinformatics Center, College of Life Sciences, Northwest A & F University , Yangling , Shaanxi , China.
authors.forename: Piar Ali
authors.initials: PA
authors.lastname: Shar
authors.affiliation: a Bioinformatics Center, College of Life Sciences, Northwest A & F University , Yangling , Shaanxi , China.
authors.forename: Weiyang
authors.initials: W
authors.lastname: Tao
authors.affiliation: a Bioinformatics Center, College of Life Sciences, Northwest A & F University , Yangling , Shaanxi , China.
authors.forename: Shuo
authors.initials: S
authors.lastname: Gao
authors.affiliation: a Bioinformatics Center, College of Life Sciences, Northwest A & F University , Yangling , Shaanxi , China.
authors.forename: Chao
authors.initials: C
authors.lastname: Huang
authors.affiliation: a Bioinformatics Center, College of Life Sciences, Northwest A & F University , Yangling , Shaanxi , China.
authors.forename: Bohui
authors.initials: B
authors.lastname: Li
authors.affiliation: a Bioinformatics Center, College of Life Sciences, Northwest A & F University , Yangling , Shaanxi , China.
authors.forename: Wenjuan
authors.initials: W
authors.lastname: Zhang
authors.affiliation: a Bioinformatics Center, College of Life Sciences, Northwest A & F University , Yangling , Shaanxi , China.
authors.forename: Mohamed
authors.initials: M
authors.lastname: Shahen
authors.affiliation: a Bioinformatics Center, College of Life Sciences, Northwest A & F University , Yangling , Shaanxi , China.
authors.forename: Chunli
authors.initials: C
authors.lastname: Zheng
authors.affiliation: a Bioinformatics Center, College of Life Sciences, Northwest A & F University , Yangling , Shaanxi , China.
authors.forename: Yaofei
authors.initials: Y
authors.lastname: Bai
authors.affiliation: a Bioinformatics Center, College of Life Sciences, Northwest A & F University , Yangling , Shaanxi , China.
authors.forename: Yonghua
authors.initials: Y
authors.lastname: Wang
mesh_terms: D008024:Ligands; D011239:Prednisolone / Q000031:analogs & derivatives* / Q000378:metabolism; D011485:Protein Binding; D060388:Support Vector Machine
chemical_list: D008024:Ligands; D011239:Prednisolone; C036266:prednylidene
keywords: Binding affinity prediction; drug target interaction; random forest; support vector machine