DeepCDA: Deep Cross-Domain Compound-Protein Affinity Prediction through LSTM and Convolutional Neural Networks

scientific article published on 27 May 2020

DeepCDA: Deep Cross-Domain Compound-Protein Affinity Prediction through LSTM and Convolutional Neural Networks is …
instance of (P31):
scholarly articleQ13442814

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P8978DBLP publication IDjournals/bioinformatics/AbbasiRPAGM20
P356DOI10.1093/BIOINFORMATICS/BTAA544
P698PubMed publication ID32462178

P2093author name stringAli Masoudi-Nejad
Massoud Amanlou
Antti Poso
Jahan B Ghasemi
Karim Abbasi
Parvin Razzaghi
P2860cites workOpen Babel: An open chemical toolboxQ21198766
SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rulesQ28090714
Deep Learning in Drug Discovery.Q38918718
SimBoost: a read-across approach for predicting drug-target binding affinities using gradient boosting machines.Q43118032
Deep Transferable Compound Representation across Domains and Tasks for Low Data Drug DiscoveryQ90988720
P921main subjectconvolutional neural networkQ17084460
long short-term memoryQ6673524
P577publication date2020-05-27
P1433published inBioinformaticsQ4914910
P1476titleDeepCDA: Deep Cross-Domain Compound-Protein Affinity Prediction through LSTM and Convolutional Neural Networks

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cites work (P2860)
Q112053034Data considerations for predictive modeling applied to the discovery of bioactive natural products
Q112705246ELECTRA-DTA: a new compound-protein binding affinity prediction model based on the contextualized sequence encoding
Q111520702FusionDTA: attention-based feature polymerizer and knowledge distillation for drug-target binding affinity prediction

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