J Chem Inf Model - Do not hesitate to use Tversky-and other hints for successful active analogue searches with feature count descriptors.

Tópicos

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{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

This study is an exhaustive analysis of the neighborhood behavior over a large coherent data set (ChEMBL target/ligand pairs of known Ki, for 165 targets with >50 associated ligands each). It focuses on similarity-based virtual screening (SVS) success defined by the ascertained optimality index. This is a weighted compromise between purity and retrieval rate of active hits in the neighborhood of an active query. One key issue addressed here is the impact of Tversky asymmetric weighing of query vs candidate features (represented as integer-value ISIDA colored fragment/pharmacophore triplet count descriptor vectors). The nearly a 3/4 million independent SVS runs showed that Tversky scores with a strong bias in favor of query-specific features are, by far, the most successful and the least failure-prone out of a set of nine other dissimilarity scores. These include classical Tanimoto, which failed to defend its privileged status in practical SVS applications. Tversky performance is not significantly conditioned by tuning of its bias parameter a. Both initial "guesses" of a = 0.9 and 0.7 were more successful than Tanimoto (at its turn, better than Euclid). Tversky was eventually tested in exhaustive similarity searching within the library of 1.6 M commercial + bioactive molecules at http://infochim.u-strasbg.fr/webserv/VSEngine.html , comparing favorably to Tanimoto in terms of "scaffold hopping" propensity. Therefore, it should be used at least as often as, perhaps in parallel to Tanimoto in SVS. Analysis with respect to query subclasses highlighted relationships of query complexity (simply expressed in terms of pharmacophore pattern counts) and/or target nature vs SVS success likelihood. SVS using more complex queries are more robust with respect to the choice of their operational premises (descriptors, metric). Yet, they are best handled by "pro-query" Tversky scores at a > 0.5. Among simpler queries, one may distinguish between "growable" (allowing for active analogs with additional features), and a few "conservative" queries not allowing any growth. These (typically bioactive amine transporter ligands) form the specific application domain of "pro-candidate" biased Tversky scores at a < 0.5.

Resumo Limpo

studi exhaust analysi neighborhood behavior larg coher data set chembl targetligand pair known ki target associ ligand focus similaritybas virtual screen svs success defin ascertain optim index weight compromis puriti retriev rate activ hit neighborhood activ queri one key issu address impact tverski asymmetr weigh queri vs candid featur repres integervalu isida color fragmentpharmacophor triplet count descriptor vector near million independ svs run show tverski score strong bias favor queryspecif featur far success least failurepron set nine dissimilar score includ classic tanimoto fail defend privileg status practic svs applic tverski perform signific condit tune bias paramet initi guess success tanimoto turn better euclid tverski eventu test exhaust similar search within librari m commerci bioactiv molecul httpinfochimustrasbgfrwebservvsenginehtml compar favor tanimoto term scaffold hop propens therefor use least often perhap parallel tanimoto svs analysi respect queri subclass highlight relationship queri complex simpli express term pharmacophor pattern count andor target natur vs svs success likelihood svs use complex queri robust respect choic oper premis descriptor metric yet best handl proqueri tverski score among simpler queri one may distinguish growabl allow activ analog addit featur conserv queri allow growth typic bioactiv amin transport ligand form specif applic domain procandid bias tverski score

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