LiSIs: An Online Scientific Workflow System for Virtual Screening

Author(s): Christos C. Kannas , Ioanna Kalvari , George Lambrinidis , Christiana M. Neophytou , Christiana G. Savva , Ioannis Kirmitzoglou , Zinonas Antoniou , Kleo G. Achilleos , David Scherf , Chara A. Pitta , Christos A. Nicolaou , Emanuel Mikros , Vasilis J. Promponas , Clarissa Gerhauser , Rajendra G. Mehta , Andreas I. Constantinou , Constantinos S. Pattichis .

Journal Name: Combinatorial Chemistry & High Throughput Screening

Volume 18 , Issue 3 , 2015

Become EABM
Become Reviewer

Abstract:

Modern methods of drug discovery and development in recent years make a wide use of computational algorithms. These methods utilise Virtual Screening (VS), which is the computational counterpart of experimental screening. In this manner the in silico models and tools initial replace the wet lab methods saving time and resources. This paper presents the overall design and implementation of a web based scientific workflow system for virtual screening called, the Life Sciences Informatics (LiSIs) platform. The LiSIs platform consists of the following layers: the input layer covering the data file input; the pre-processing layer covering the descriptors calculation, and the docking preparation components; the processing layer covering the attribute filtering, compound similarity, substructure matching, docking prediction, predictive modelling and molecular clustering; post-processing layer covering the output reformatting and binary file merging components; output layer covering the storage component. The potential of LiSIs platform has been demonstrated through two case studies designed to illustrate the preparation of tools for the identification of promising chemical structures. The first case study involved the development of a Quantitative Structure Activity Relationship (QSAR) model on a literature dataset while the second case study implemented a docking-based virtual screening experiment. Our results show that VS workflows utilizing docking, predictive models and other in silico tools as implemented in the LiSIs platform can identify compounds in line with expert expectations. We anticipate that the deployment of LiSIs, as currently implemented and available for use, can enable drug discovery researchers to more easily use state of the art computational techniques in their search for promising chemical compounds. The LiSIs platform is freely accessible (i) under the GRANATUM platform at: http://www.granatum.org and (ii) directly at: http://lisis.cs.ucy.ac.cy.

Keywords: Chemoinformatics, docking, drug discovery, predictive models, QSAR, scientific workflow, virtual screening.

Rights & PermissionsPrintExport Cite as

Article Details

VOLUME: 18
ISSUE: 3
Year: 2015
Page: [281 - 295]
Pages: 15
DOI: 10.2174/1386207318666150305123341
Price: $58

Article Metrics

PDF: 24