Interaction prediction in structure-based virtual screening using deep learning.
Comput Biol Med
; 100: 253-258, 2018 09 01.
Article
in En
| MEDLINE
| ID: mdl-28941550
ABSTRACT
We introduce a deep learning architecture for structure-based virtual screening that generates fixed-sized fingerprints of proteins and small molecules by applying learnable atom convolution and softmax operations to each molecule separately. These fingerprints are further non-linearly transformed, their inner product is calculated and used to predict the binding potential. Moreover, we show that widely used benchmark datasets may be insufficient for testing structure-based virtual screening methods that utilize machine learning. Therefore, we introduce a new benchmark dataset, which we constructed based on DUD-E, MUV and PDBBind databases.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Proteins
/
Databases, Protein
/
Deep Learning
Type of study:
Diagnostic_studies
/
Prognostic_studies
/
Risk_factors_studies
/
Screening_studies
Language:
En
Journal:
Comput Biol Med
Year:
2018
Type:
Article