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.
Combinatorial Chemistry & High Throughput Screening
Title:LiSIs: An Online Scientific Workflow System for Virtual Screening
Volume: 18 Issue: 3
Author(s): David Scherf, Constantinos S. Pattichis, Andreas I. Constantinou, Rajendra G. Mehta, Clarissa Gerhauser, Vasilis J. Promponas, Emanuel Mikros, Christos A. Nicolaou, Chara A. Pitta, Christos C. Kannas, Kleo G. Achilleos, Zinonas Antoniou, Ioannis Kirmitzoglou, Christiana G. Savva, Christiana M. Neophytou, George Lambrinidis and Ioanna Kalvari
Affiliation:
Keywords: Chemoinformatics, docking, drug discovery, predictive models, QSAR, scientific workflow, virtual screening.
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.
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Cite this article as:
Scherf David, S. Pattichis Constantinos, I. Constantinou Andreas, G. Mehta Rajendra, Gerhauser Clarissa, J. Promponas Vasilis, Mikros Emanuel, A. Nicolaou Christos, A. Pitta Chara, C. Kannas Christos, G. Achilleos Kleo, Antoniou Zinonas, Kirmitzoglou Ioannis, G. Savva Christiana, M. Neophytou Christiana, Lambrinidis George and Kalvari Ioanna, LiSIs: An Online Scientific Workflow System for Virtual Screening, Combinatorial Chemistry & High Throughput Screening 2015; 18 (3) . https://dx.doi.org/10.2174/1386207318666150305123341
DOI https://dx.doi.org/10.2174/1386207318666150305123341 |
Print ISSN 1386-2073 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5402 |
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