Hello Miclos,

Thank you very much for your comments.

Actually, compr in combination with JKlustor provides plenty and sufficient number of options for my analyzis - ChemAxon team makes marvelous software.

However, I would like to draw your attention to two additional papers:

1) Hassan Rezaei, Masashi Emoto, and Masao Mukaidono - New Similarity Measure Between Two Fuzzy Sets - Journal of Advanced Computational Intelligence and Intelligent Informatics 2006, 10(6), 946-953.

2) Amos Tversky - Features of similarity - Psychological Review 1977, 84(4), 327-352.

I do hope that you will not consider me rude for this, I really like how compr works, however, there are some descripansies in the final summary.

First paper proposes an approach that might help to solve the symmetry issue for two sets of a different dimension.

And the second - the seminal work on similarity - discusses problems of

symmetry in similarity more broadly. Unfortunately, in cheminformatics community it is sometimes overlooked that if two sets A(N), B(M) have N>M -

then their similarity assessment S(A,B) != S(B,A), while normalization

to calculate an average distance between their centers of weight often

is quite meaningless.

As I pointed out erlier (see my previous posts), while compr works excellent for estimation of the diversity of a library , I found some unexpected results comparing two different sets, espesially if they differ in their dimensions, nevertheless, compr provides with option -z plenty of data for accurate analysis of different sets.

It seems to me, that assymetrical calculation provides more useful information:

S(A,B) - estimation of a redundancy degree of the set A relatively to the set B

S(B,A) - estimation of a congruency degree of the set B relatively to a continuity of subsets of the set A

And I am very thankful and excited that compr enables me to do such calculations changing order of input files.

What would be really helpful is also to be able to control optionally listing up to 3 nnbs of minD to nearest neighbours.

Thanks a lot,

Lex