package lbvs_consent

Consent

Chemoinformatics software for Ligand-Based Virtual Screening (LBVS) using consensus queries.

Cf. the INSTALL file for instructions on how to install consent.

I) Command line help

lbvs_consent -s {sing|oppo|opti|real|know}
             -q queries.{sdf|mol2|csv|ecfp4}
             -db candidates.{sdf|mol2|csv|ecfp4}

  -s <pol> consensus policy {sing|oppo|opti|real|know} (mandatory)
  -q <filename> queries file (known actives; mandatory)
  -db <filename> database to rank order (mandatory)
  -o <filename> where to write scores (can be combined with -top)
  -n <int> consensus size; #known actives used to create query (optional;
           default=all molecules in query file)
  -top <int> how many top scoring molecules to write out (optional;
       default=all; must be combined with -o)

II) Usage recommendation

Please cite the corresponding paper (https://doi.org/10.1186/s13321-017-0248-5) in case you use this software and publish about your results (Consensus queries in ligand-based virtual screening experiments. F. Berenger, O. Vu and J., Meiler. Journal of Cheminformatics, November 2017).

The opportunist consensus policy (-s oppo) is recommended. It works well with any fingerprint and is usually the best performing method.

However, if you really need to go faster, here are some recommendations:

• MACCS fingerprint (166 bits): use the realistic policy (-s real); it will average the MACCS fingerprints of your known actives.

• ECFP4 fingerprint (2048 bits; folded; uncounted): use the optimist policy (-s opti); it will do a logical union of the fingerprints of your known actives.

• UMOP2D (unfolded MOLPRINT2D; uncounted): same as for ECFP4, use -s opti.

III) How to encode your molecules

First, we need some SDF and MOL2 files. The obabel command is provided by the Open Babel package (cf. http://openbabel.org).

obabel data/ARm_actives.smi -O data/ARm_actives.sdf
obabel data/ARm_inactives.smi -O data/ARm_inactives.sdf
obabel data/ARm_actives.smi -O data/ARm_actives.mol2
obabel data/ARm_inactives.smi -O data/ARm_inactives.mol2
cat data/ARm_actives.mol2 data/ARm_inactives.mol2 > data/ARm_database.mol2

With the MACCS fingerprint

lbvs_consent_ob_maccs data/ARm_actives.sdf > data/ARm_actives.maccs
lbvs_consent_ob_maccs data/ARm_inactives.sdf > data/ARm_inactives.maccs
cat data/ARm_actives.maccs data/ARm_inactives.maccs > data/ARm_database.maccs

With the ECFP4 fingerprint

lbvs_consent_ecfp4.py data/ARm_actives.sdf > data/ARm_actives.ecfp4
lbvs_consent_ecfp4.py data/ARm_inactives.sdf > data/ARm_inactives.ecfp4
cat data/ARm_actives.ecfp4 data/ARm_inactives.ecfp4 > data/ARm_database.ecfp4

With the UMOP2D fingerprint

lbvs_consent_mop2di -i data/ARm_database.mol2 > data/ARm_database.idx
lbvs_consent_mop2de -idx data/ARm_database.idx -i data/ARm_database.mol2 -o data/ARm_database.mop2d

IV) How to query with a consensus query and a consensus policy

# example with ECFP4 fingerprints and 20 actives
head -20 data/ARm_actives.ecfp4 > data/ARm_query_20.ecfp4
# recommended way; AUC ~= 0.60
lbvs_consent -s oppo -q data/ARm_query_20.ecfp4 -db data/ARm_database.ecfp4 -o scores.txt
# faster, but still with good performance in many cases; AUC ~= 0.61
lbvs_consent -s opti -q data/ARm_query_20.ecfp4 -db data/ARm_database.ecfp4 -o scores.txt