ArticleHeliyon2020
FRnet-DTI: Deep convolutional neural network for drug-target interaction prediction.
Article in Heliyon, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Sequence-based prediction of drug-target binding using machine learning, deep learning and ensemble models without 3D structural information.Scientific reports · 2026Article
- A comprehensive review of neural network-based approaches for drug-target interaction prediction.Molecular diversity · 2026Review
- InceptionDTA: Predicting drug-target binding affinity with biological context features and inception networks.Heliyon · 2025Article
- Improving drug repositioning with negative data labeling using large language models.Journal of cheminformatics · 2025Article
- Prediction of drug target interaction based on under sampling strategy and random forest algorithm.PloS one · 2025Article
- Predicting drug protein interactions based on improved support vector data description in unbalanced data.BioImpacts : BI · 2025Article
- The changing scenario of drug discovery using AI to deep learning: Recent advancement, success stories, collaborations, and challenges.Molecular therapy. Nucleic acids · 2024Review
- Drug-target interaction prediction based on protein features, using wrapper feature selection.Scientific reports · 2023Article
- Detecting Drug-Target Interactions with Feature Similarity Fusion and Molecular Graphs.Biology · 2022Article
- DeepMHADTA: Prediction of Drug-Target Binding Affinity Using Multi-Head Self-Attention and Convolutional Neural Network.Current issues in molecular biology · 2022Article
- Review
- Multi-TransDTI: Transformer for Drug-Target Interaction Prediction Based on Simple Universal Dictionaries with Multi-View Strategy.Biomolecules · 2022Article
- Deep Neural Network-Assisted Drug Recommendation Systems for Identifying Potential Drug-Target Interactions.ACS omega · 2022Article
- Heterogeneous network propagation with forward similarity integration to enhance drug-target association prediction.PeerJ. Computer science · 2022Article
- Binding affinity prediction for binary drug-target interactions using semi-supervised transfer learning.Journal of computer-aided molecular design · 2021Article
- Logistic matrix factorisation and generative adversarial neural network-based method for predicting drug-target interactions.Molecular diversity · 2021Article
- Convolutional neural networks (CNNs): concepts and applications in pharmacogenomics.Molecular diversity · 2021Article
- Predicting Drug-Target Interactions Based on the Ensemble Models of Multiple Feature Pairs.International journal of molecular sciences · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The task of drug-target interaction prediction holds significant importance in pharmacology and therapeutic drug design. In this paper, we present FRnet-DTI, an auto-encoder based feature manipulation and a convolutional neural network based classifier for drug target interaction prediction. Two convolutional neural networks are proposed: FRnet-Encode and FRnet-Predict. Here, one model is used for feature manipulation and the other one for classification. Using the first method FRnet-Encode, we generate 4096 features for each of the instances in each of the datasets and use the second method, FRnet-Predict, to identify interaction probability employing those features. We have tested our method on four gold standard datasets extensively used by other researchers. Experimental results shows that our method significantly improves over the state-of-the-art method on three out of four drug-target interaction gold standard datasets on both area under curve for Receiver Operating Characteristic (auROC) and area under Precision Recall curve (auPR) metric. We also introduce twenty new potential drug-target pairs for interaction based on high prediction scores. The source codes and implementation details of our methods are available from https://github.com/farshidrayhanuiu/FRnet-DTI/ and also readily available to use as an web application from http://farshidrayhan.pythonanywhere.com/FRnet-DTI/.
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