Computational Models for Tuberculosis Drug Discovery

  • Sean Ekins, Joel S. Freundlich
  • January 2013, Springer Science + Business Media
  • DOI: 10.1007/978-1-62703-342-8_16

Models for TB drug discovery

What is it about?

A brief review of QSAR, machine learning and structure based approaches used with m. tuberculosis. How can we leverage the HTS data in the public domain?

Why is it important?

Productivity in bringing new drugs to the patient for TB has been poor. Increasing drug resistance combined with lack of funding suggests we should be more thoughtful in using data generated by others previously to learn and also to prevent repetition. Many groups have built small local models in past, few had generated models on phenotypic screening data.

Perspectives

Dr Sean Ekins
Collaborations in Chemistry

The review of computational methods used in TB drug discovery is probably also generally applicable to other neglected diseases facing similar challenges of drug resistance, few clinical options and poor funding.

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http://dx.doi.org/10.1007/978-1-62703-342-8_16

The following have contributed to this page: Dr Sean Ekins and Joel Freundlich