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[] Intelligent consensus predictor: Towards more precise predictions for external set compounds

1 Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700 032, India
2 Interdisciplinary Center for Nanotoxicity, Department of Chemistry and Biochemistry, Jackson State University, Jackson, MS-39217, USA
* Author to whom correspondence should be addressed.
11 October 2017
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Abstract

Quantitative structure-activity relationship (QSAR) modeling has travelled a long journey in drug discovery process as well as in prediction of property and/or toxicity data of diverse chemicals in order to fill the data gaps. The goodness-of-fit and quality of a  model and its prediction capability for untested compounds are assessed through diverse validation metrics. There is a constant endeavor among QSAR researchers to get better the quality of predictions for lowering the predicted residuals for external compounds. The objective of the present study has been to improve the prediction quality for external compounds with implication of “intelligent” consensus modeling approach. Three different forms of consensus models were developed for six different datasets to explore their prediction capability on query chemicals. The types are average of predictions from all qualifying individual models (CM1), weighted average predictions from all qualifying individual models (CM2), and best selection of predictions (compound-wise) from individual models (CM3). Among three consensus models, newer strategies like CM2 and CM3 are evolved as the “winners” considering prediction errors of query compounds for the studied six data sets irrespective of diverse responses, number of data points as well as dissimilar modeling algorithm. We have also developed a tool named “Intelligent Consensus Predictor” which is freely accessible via the web http://teqip.jdvu.ac.in/QSAR_Tools/ and http://dtclab.webs.com/software-tools. The details of this work have been presented in Conferentia Chemometrica http://cc2017.ttk.mta.hu/ in Hungary during September 3-6, 2017.

References

  1. Dearden JC. The history and development of quantitative structure-activity relationships (QSARs). IJQSPR. 2016;1(1):1–44.
  2. Roy K, Kar S, Das RN. Understanding the basics of QSAR for applications in pharmaceutical sciences and risk assessment. Academic press. 2015.
  3. http://teqip.jdvu.ac.in/QSAR_Tools/
  4. http://dtclab.webs.com/software-tools
  5. Roy K, Das RN, Ambure P, Aher RB. Be aware of error measures. Further studies on validation of predictive QSAR models. Chemom Intell Lab Syst. 2016;152:18-33.
  6. Roy K, Ambure P, Kar S, Ojha PK, Is it possible to improve the quality of predictions from an intelligent” use of multiple QSAR/QSPR/QSTR models? J Chemom 2017 (Submitted)

Keywords

QSAR, Consensus predictions, Validation

Cite this article as

Roy, K.; Ambure, P.; Kar, S.; Ojha, P. Intelligent consensus predictor: Towards more precise predictions for external set compounds. In Proceedings of the MOL2NET 2017, International Conference on Multidisciplinary Sciences, 3rd edition, 15 February–20 December 2017; Sciforum Electronic Conference Series, Vol. 3, 2017 ; doi:10.3390/mol2net-03-04619

Presentation

Author biographies

Kunal Roy
Dr. Kunal Roy (https://sites.google.com/site/kunalroyindia/) is a Professor in the Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India. He has been a recipient of Commonwealth Academic Staff Fellowship (University of Manchester, 2007) and Marie Curie International Fellowship (University of Manchester, 2013). The field of his research interest is QSAR and Molecular Modeling. Dr. Roy has published more than 250 research papers in refereed journals. He has also coauthored QSAR related books entitled “Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment” (Elsevier, 2015) and “A Primer on QSAR/QSPR Modeling: Fundamental Concepts” (Springer, 2015) and edited “Quantitative Structure-Activity Relationships in Drug Design, Predictive Toxicology, and Risk Assessment” (IGI Global, 2015). His current h-index is 35 (SCOPUS). Prof. Roy is the editor-in-Chief of International Journal of Quantitative Structure-Property Relationships (IJQSPR) [IGI Global] and an Associate Editor of Molecular Diversity [Springer] . He is also a member of the Editorial Advisory Boards of (1) European Journal of Medicinal Chemistry [Elsevier], (2) Journal of Molecular Graphics and Modelling [Elsevier] (3) Chemical Biology and Drug Design [Wiley], (4) Expert Opinion on Drug Discovery [Informa], (5) Letters in Drug Design and Discovery [Bentham], (6) Current Computer-Aided Drug Design [Bentham]. Prof. Roy is also a recipient of several awards including Bioorganic and Medicinal Chemistry Most Cited Paper 2003-2006, 2004-2007 and 2006-2009 Awards from Elsevier, The Netherlands, Bioorganic and Medicinal Chemistry Letters Most Cited Paper 2006-2009 Award from Elsevier, The Netherlands.

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