Two-way clustering method for QSAR modeling of diverse set of chemicals
An alternative approach is the use of computational toxicology tolls which use computed properties of the mutagens to predict their mutagenic potential using mathematical models. During the past five decades or so, mathematical chemistry has developed many new molecular descriptors. At the same time the power of computer has progress steadily following Moore's law. Currently we are witnessing an upsurge of research fueled by two important factors: a) Novel applications of mathematics to chemical and biological systems and ii) Availability of software which allows hypothesis driven as well as discovery oriented research within a reasonable time frame. This trend of research has led to numerous useful applications to scientifically, socially, technologically, and economically important areas such as drug discovery, protection of human as well as ecological health.
The articles by Basak, Majumdar, and Grunwald developed in silico models for the estimation of potential mutagenicity of chemicals from their structure without the input of any other experimental data. Such models can also be used to prioritize laboratory testing based on estimated values.
Original publication
Most read news
Original publication
Subhabrata Majumdar, Subhash C. Basak and Gregory D. Grunwald; "Adapting Interrelated Two-Way Clustering Method for Quantitative Structure-Activity Relationship (QSAR) Modeling of Mutagenicity/Non- Mutagenicity of a Diverse Set of Chemicals"; Curretn cOmputer Aided-Drug Design; 2016
Organizations
Other news from the department science
Get the life science industry in your inbox
From now on, don't miss a thing: Our newsletter for biotechnology, pharma and life sciences brings you up to date every Tuesday and Thursday. The latest industry news, product highlights and innovations - compact and easy to understand in your inbox. Researched by us so you don't have to.