Combining Computational Methods for Hit to Lead Optimization in Mycobacterium Tuberculosis Drug Discovery

  • Sean Ekins, Joel S. Freundlich, Judith V. Hobrath, E. Lucile White, Robert C. Reynolds
  • Pharmaceutical Research, October 2013, Springer Science + Business Media
  • DOI: 10.1007/s11095-013-1172-7

Hit to lead and Machine learning

What is it about?

This paper describes using 3 dose response and cytotoxicity bayesian models for TB to predict 1924 additional molecules. This represents external testing on a pretty big scale.

Why is it important?

We sho the value of these models for predicting compounds from our lab and from the literature. We generally see >10 fold enrichment.

Read Publication

http://dx.doi.org/10.1007/s11095-013-1172-7

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