Archive for the ‘QSAR’ Category

Quantitative structure-activity relationship (QSAR)

Monday, August 15th, 2011

I ran across enough materials while researching AZOrange that I needed to make a separate post on QSAR:

The Cheminformatics and QSAR Society

An Introduction to QSAR Methodology by Allen B. Richon

Quantitative structure-activity relationship – Wikipedia

QSAR World

Of greatest interest for people involved in cheminformatics, toxicity, drug development, etc.

Subject identity cuts across every field.

Now that would be an ambitious and interesting book, “Subject Identity.” An edited volume with contributions from experts in a variety of fields.

International QSAR Foundation

Tuesday, August 2nd, 2011

International QSAR Foundation

From the website:

The International QSAR Foundation is the only nonprofit research organization devoted solely to creating alternative methods for identifying chemical hazards without further laboratory testing.

We develop, implement and support new QSAR technologies for use in regulation, research and education or wherever testing animals with chemicals is now required. QSAR models predict chemical behavior directly from chemical structure and simulate adverse effects in cells, tissues and lab animals.

When combined with other alternative test methods, QSAR can minimize the the need for animal tests while improving safe use of drugs and other chemicals. (emphasis added)

Subject identification by predicted behavior anyone?

QSAR Toolbox

Tuesday, August 2nd, 2011

QSAR Toolbox

From the website:

The category approach used in the Toolbox:

  • Focuses on intrinsic properties of chemicals (mechanism or mode of action, (eco-)toxicological effects).
  • Allows for entire categories of chemicals to be assessed when only a few members are tested, saving costs and the need for testing on animals.
  • Enables robust hazard assessment through mechanistic comparisons without testing.

The QSAR Toolbox is a software intended to be used by governments, the chemical industry and other stakeholders to fill gaps in (eco-)toxicity data needed for assessing the hazards of chemicals. The Toolbox incorporates information and tools from various sources into a logical workflow. Grouping chemicals into chemical categories is crucial to this workflow.

AZOrange – Machine Learning for QSAR Modeling

Friday, July 29th, 2011

AZOrange – High performance open source machine learning for QSAR modeling in a graphical programming environment Jonna C Stalring, Lars A Carlsson, Pedro Almeida and Scott Boyer. Journal of Cheminformatics 2011, 3:28doi:10.1186/1758-2946-3-28


Machine learning has a vast range of applications. In particular, advanced machine learning methods are routinely and increasingly used in quantitative structure activity relationship (QSAR) modeling. QSAR data sets often encompass tens of thousands of compounds and the size of proprietary, as well as public data sets, is rapidly growing. Hence, there is a demand for computationally efficient machine learning algorithms, easily available to researchers without extensive machine learning knowledge. In granting the scientific principles of transparency and reproducibility, Open Source solutions are increasingly acknowledged by regulatory authorities. Thus, an Open Source state-of-the-art high performance machine learning platform, interfacing multiple, customized machine learning algorithms for both graphical programming and scripting, to be used for large scale development of QSAR models of regulatory quality, is of great value to the QSAR community.

Project homepage: AZOrange (Ubuntu, I assume it compiles and runs on other *nix platforms. I run Ubuntu so I need to setup another *nix distribution just for test purposes.)