This provides tools relating to mapping pharmaceutical lead optimization campaigns. The initial implementation is focused on planning free energy calculations to span a library of inhibitors by computing relative free energies between related inhibitors, but the scope of the mapper will likely expand with time. This toolkit:

- is written in Python
- turns the problem of planning relative free energy calculations within a library into a graph theory problem
- outputs a map of planned calculations

This is written for computational scientists working in the pharmaceutical industry generally, including academia, industry, and elsewhere, who need tools to help plan lead optimization campaigns.

The code is being released under the BSD license and hopefully will be a community effort.

Please contact David Mobley and Shuai Liu if you need any help getting this to work or any clarification on installing, etc.

If you use this, please cite our paper in JCAMD: http://link.springer.com/article/10.1007%2Fs10822-013-9678-y