Imputation of Assay Bioactivity Data Using Deep Learning.
J Chem Inf Model
; 59(3): 1197-1204, 2019 03 25.
Article
in En
| MEDLINE
| ID: mdl-30753070
ABSTRACT
We describe a novel deep learning neural network method and its application to impute assay pIC50 values. Unlike conventional machine learning approaches, this method is trained on sparse bioactivity data as input, typical of that found in public and commercial databases, enabling it to learn directly from correlations between activities measured in different assays. In two case studies on public domain data sets we show that the neural network method outperforms traditional quantitative structure-activity relationship (QSAR) models and other leading approaches. Furthermore, by focusing on only the most confident predictions the accuracy is increased to R2 > 0.9 using our method, as compared to R2 = 0.44 when reporting all predictions.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Pharmaceutical Preparations
/
Deep Learning
Type of study:
Prognostic_studies
Language:
En
Journal:
J Chem Inf Model
Journal subject:
INFORMATICA MEDICA
/
QUIMICA
Year:
2019
Document type:
Article
Affiliation country:
Reino Unido