P
Pablo R. Duchowicz
Researcher at National Scientific and Technical Research Council
Publications - 78
Citations - 899
Pablo R. Duchowicz is an academic researcher from National Scientific and Technical Research Council. The author has contributed to research in topics: Quantitative structure–activity relationship & Molecular descriptor. The author has an hindex of 17, co-authored 77 publications receiving 762 citations. Previous affiliations of Pablo R. Duchowicz include Mayo Clinic.
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QSPR studies on refractive indices of structurally heterogeneous polymers
Pablo R. Duchowicz,Silvina E. Fioressi,Daniel E. Bacelo,Laura M. Saavedra,Alla P. Toropova,Andrey A. Toropov +5 more
TL;DR: A predictive Quantitative Structure–Property Relationship (QSPR) for the refractive indices of 234 structurally diverse polymers is developed and could be useful for assisting the development of new polymeric materials.
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Quantitative structure-activity relationships of mosquito larvicidal chalcone derivatives.
Gustavo Antonio Pasquale,Gustavo P. Romanelli,Juan Carlos Autino,Javier Garcia,Erlinda V. Ortiz,Pablo R. Duchowicz +5 more
TL;DR: The mosquito larvicidal activities of a series of chalcones and some derivatives were subjected to a quantitative structure-activity relationship (QSAR) study, using more than a thousand constitutional, topological, geometrical, and electronic molecular descriptors calculated with Dragon software.
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An integrated drug development approach applying topological descriptors.
Alan Talevi,Carolina Leticia Bellera,Mauricio E. Di Ianni,Pablo R. Duchowicz,Luis E. Bruno-Blanch,Eduardo A. Castro +5 more
TL;DR: The opportunities posed by computer-assisted drug design in the light of two aspects of the current drug discovery scenario: the decline of innovation due to high attrition rates at clinical stage of development and the combinatorial explosion emerging from exponential growth of feasible small molecules and genome and proteome exploration.
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QSAR Study for Carcinogenicity in a Large Set of Organic Compounds
TL;DR: The novelty of present work relies on the development of an alternative predictive structure-carcinogenicity relationship in a large heterogeneous set of organic compounds, by only using a reduced number of geometry independent molecular descriptors.
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pKa modeling and prediction of a series of pH indicators through genetic algorithm-least square support vector regression
TL;DR: In this paper, the pK a values of a series of 107 indicators have been modeled by means of a quantitative structure-property relationship (QSPR) approach based on physicochemical descriptors and different variable selection and regression methods.