title: Virtual drug screen schema based on multiview similarity integration and ranking aggregation.
abstract: The current drug virtual screen (VS) methods mainly include two categories. i.e., ligand/target structure-based virtual screen and that, utilizing protein-ligand interaction fingerprint information based on the large number of complex structures. Since the former one focuses on the one-side information while the later one focuses on the whole complex structure, they are thus complementary and can be boosted by each other. However, a common problem faced here is how to present a comprehensive understanding and evaluation of the various virtual screen results derived from various VS methods. Furthermore, there is still an urgent need for developing an efficient approach to fully integrate various VS methods from a comprehensive multiview perspective. In this study, our virtual screen schema based on multiview similarity integration and ranking aggregation was tested comprehensively with statistical evaluations, providing several novel and useful clues on how to perform drug VS from multiple heterogeneous data sources. (1) 18 complex structures of HIV-1 protease with ligands from the PDB were curated as a test data set and the VS was performed with five different drug representations. Ritonavir ( 1HXW ) was selected as the query in VS and the weighted ranks of the query results were aggregated from multiple views through four similarity integration approaches. (2) Further, one of the ranking aggregation methods was used to integrate the similarity ranks calculated by gene ontology (GO) fingerprint and structural fingerprint on the data set from connectivity map, and two typical HDAC and HSP90 inhibitors were chosen as the queries. The results show that rank aggregation can enhance the result of similarity searching in VS when two or more descriptions are involved and provide a more reasonable similarity rank result. Our study shows that integrated VS based on multiple data fusion can achieve a remarkable better performance compared to that from individual ones and, thus, serves as a promising way for efficient drug screening, taking advantages of the rapidly accumulated molecule representations and heterogeneous data in the pharmacological area.
authors.affiliation: School of Life Sciences and Technology, Tongji University, 200092, China.
authors.forename: Hong
authors.initials: H
authors.lastname: Kang
authors.forename: Zhen
authors.initials: Z
authors.lastname: Sheng
authors.forename: Ruixin
authors.initials: R
authors.lastname: Zhu
authors.forename: Qi
authors.initials: Q
authors.lastname: Huang
authors.forename: Qi
authors.initials: Q
authors.lastname: Liu
authors.forename: Zhiwei
authors.initials: Z
authors.lastname: Cao
mesh_terms: D000465:Algorithms; D030562:Databases, Protein; D004353:Drug Evaluation, Preclinical / Q000379:methods*; D016333:HIV Protease / Q000378:metabolism; D017320:HIV Protease Inhibitors / Q000378:metabolism / Q000494:pharmacology; D015497:HIV-1 / Q000201:enzymology; D057166:High-Throughput Screening Assays; D006801:Humans; D008024:Ligands; D009010:Monte Carlo Method; D014584:User-Computer Interface
chemical_list: D017320:HIV Protease Inhibitors; D008024:Ligands; D016333:HIV Protease