Orthologue chemical space and its influence on target prediction.
Bioinformatics
; 34(1): 72-79, 2018 01 01.
Article
em En
| MEDLINE
| ID: mdl-28961699
ABSTRACT
Motivation In silico approaches often fail to utilize bioactivity data available for orthologous targets due to insufficient evidence highlighting the benefit for such an approach. Deeper investigation into orthologue chemical space and its influence toward expanding compound and target coverage is necessary to improve the confidence in this practice. Results:
Here we present analysis of the orthologue chemical space in ChEMBL and PubChem and its impact on target prediction. We highlight the number of conflicting bioactivities between human and orthologues is low and annotations are overall compatible. Chemical space analysis shows orthologues are chemically dissimilar to human with high intra-group similarity, suggesting they could effectively extend the chemical space modelled. Based on these observations, we show the benefit of orthologue inclusion in terms of novel target coverage. We also benchmarked predictive models using a time-series split and also using bioactivities from Chemistry Connect and HTS data available at AstraZeneca, showing that orthologue bioactivity inclusion statistically improved performance. Availability and implementation Orthologue-based bioactivity prediction and the compound training set are available at www.github.com/lhm30/PIDGINv2. Contact ab454@cam.ac.uk. Supplementary information Supplementary data are available at Bioinformatics online.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Simulação por Computador
/
Proteínas
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Homologia de Sequência de Aminoácidos
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Biologia Computacional
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Descoberta de Drogas
Tipo de estudo:
Prognostic_studies
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Risk_factors_studies
Limite:
Animals
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Humans
Idioma:
En
Revista:
Bioinformatics
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2018
Tipo de documento:
Article
País de afiliação:
Reino Unido