Abstract: Doubting on the accuracy of fingerprint based molecule substructure search, I did a test between different fingerprints implemented in CDK and subgraph isomorphism. The result is very interesting, CDK fingerprints should never be used alone in substructure search, but combine CDK fingerprints and subgraph isomorphism, we can have a balance between speed and accuracy.

Backgournd

Guha wrote a post about benchmarking of different type of fingerprints with benchmarking strategies described by Bender & Glen and Godden et al. The benchmark is based on Tanimoto similarity, which is the foundation of most chemistry database’s similar structure search. Another impo

rtant feature of molecule structure search is substructure, currently, subgraph isomorphism and fingerprint are both used in substructure search. Adel Golovin and Kim Henrick’s article Chemical Substructure Search in SQL provides a pure SQL subgraph isomorphism strategies, Rich Apodaca’s post fingerprint based MySQL substructure search in MySQL also found a solution with limited binary operation in MySQL.

Fingerprint is obviously faster as it’s much less consuming than subgraph isomorphism. But the real question is, does the fingerprint method really find all the substructures? Are there any hits misjudged as substructure?

Method

Using DrugBank small molecule drugs as test dataset, several hand-draw structures of different types as queries, I performed substructure search using subgraph isomorphism and fingerprints implemented in CDK. If a hit in search results of fingerprint method is also found in search results of subgraph isomorphism method, I count this hit as a correct hit, other wise the hit will be counted as a incorrect hit.

All fingerprints implemented in CDK are tested, generated using Fingerprinter, ExtendedFingerprinter, GraphOnlyFingerprinter, SubstructureFingerprinter, MACCSFingerprinter and EStateFingerprinter. All parameters are kept default.

To test the accuracy of queries with different complexity, I drew several structures, as listed below.

Result

The result is listed below. The result is listed in the format of “A/B”. A represents current hits, i.e. hits also founded by subgraph isomorphism method. B represents incurrent hits. For example, 31/49 stands for 31 current hits and 49 incurrent hits are found. Higher A and lower B is better.

#	Query	Subgraph Isomorphism	EState	MACCS	Standard CDK	Entended CDK	Graphonly CDK FIngerpint	Substructure Fingerprint
1		31	0/0	0/43	31/49	31/54	31/574	0/0
2		54	0/0	0/1	21/95	18/84	54/1512	11/30
3		29	0/0	0/20	29/74	29/83	29/1793	0/5
4		3	0/0	0/85	3/6	3/3	3/36	0/4
5		31	0/0	29/93	31/14	31/13	31/1593	27/53
6		23	0/0	0/0	23/0	23/0	23/23	0/0
7		9	0/0	0/0	8/83	8/63	9/237	5/40

Conclusions & Questions

As we can see from the table, MACCS, Estate and Substructure Fingerprint  perform very bad, they found very little hit, sometimes no hit at all. They are not designed to do this task, it’s not amazing to see this result.

For standard and extended CDK fingerprint, sometimes standard one works better(Maybe I should use longer extended fingerprint rather than the default length, as discussed in Guha’s post). On queries of complex ring system, extended CDK fingerprint works better, but not a obvious advantage.

But I wonder why hashed fingerprint still miss some result, (see query 2 and query 7)? Why the superstructure doesn’t share the same bits with substructure? Is this because the structure is too simple?

As many incurrent hits are found, CDK fingerprints should never be used alone in substructure search. But please consider combine CDK fingerprints and subgraph isomorphism, do fingerprint search first, we can avoid performing the consuming subgraph isomorphism match on all targets, thus we can have a balance between speed and accuracy in that way.