Improving (Q)SAR predictions by examining bias in the selection of compounds for experimental testing.
SAR QSAR Environ Res
; 30(10): 759-773, 2019 Oct.
Article
in En
| MEDLINE
| ID: mdl-31547686
ABSTRACT
Existing data on structures and biological activities are limited and distributed unevenly across distinct molecular targets and chemical compounds. The question arises if these data represent an unbiased sample of the general population of chemical-biological interactions. To answer this question, we analyzed ChEMBL data for 87,583 molecules tested against 919 protein targets using supervised and unsupervised approaches. Hierarchical clustering of the Murcko frameworks generated using Chemistry Development Toolkit showed that the available data form a big diffuse cloud without apparent structure. In contrast hereto, PASS-based classifiers allowed prediction whether the compound had been tested against the particular molecular target, despite whether it was active or not. Thus, one may conclude that the selection of chemical compounds for testing against specific targets is biased, probably due to the influence of prior knowledge. We assessed the possibility to improve (Q)SAR predictions using this fact PASS prediction of the interaction with the particular target for compounds predicted as tested against the target has significantly higher accuracy than for those predicted as untested (average ROC AUC are about 0.87 and 0.75, respectively). Thus, considering the existing bias in the data of the training set may increase the performance of virtual screening.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Structure-Activity Relationship
/
Drug Discovery
Type of study:
Prognostic_studies
/
Risk_factors_studies
Language:
En
Journal:
SAR QSAR Environ Res
Journal subject:
SAUDE AMBIENTAL
Year:
2019
Document type:
Article
Affiliation country:
RUSSIA