I’ve uploaded a new version of fingerprint (v 3.4) which now supports feature fingerprints – fingerprints that are represented as variable length vectors of numbers or strings. An example would be circular fingerprints. Now, when reading fingerprints you have to indicate whether you’re loading binary fingerprints or not (via the binary argument in fp.read). A new line parser function (ecfp.lf) is provided to load these types of files, though it’s trivial to write your own. Similarity can be evaluated between feature fingerprints in the usual manner, but the metrics are restricted to Tanimoto and Dice. A function is also available to convert a collection of feature fingerprints into a set of fixed length binary fingerprints (featvec.to.binaryfp) as described here.
- Correct formula generation
- Correct handling of SD tags whose values are just white space
- Proper generation of Murcko frameworks when molecule objects are requested
- 3 new descriptors – FMF, acidic group count, basic group count