scholarly journals ELECTRA-DTA: A New Compound-protein Binding Affinity Prediction Model based on the Contextualized Sequence Encoding

2021 ◽  
Author(s):  
junjie wang ◽  
NaiFeng Wen ◽  
Chunyu Wang ◽  
Lingling Zhao ◽  
Liang Cheng
Keyword(s):  
Drug Discovery ◽  
Binding Affinity ◽  
Drug Target ◽  
Large Scale ◽  
Prediction Models ◽  
Target Selection ◽  
Drug Repurposing ◽  
Representation Learning ◽  
Search Space ◽  
Feature Representation

Abstract Motivation: Drug-target binding affinity (DTA) reflects the strength of the drug-target interaction; therefore, predicting the DTA can considerably benefit drug discovery by narrowing the search space and pruning drug-target (DT) pairs with low binding affinity scores. Representation learning using deep neural networks has achieved promising performance compared with traditional machine learning methods; hence, extensive research efforts have been made in learning the feature representation of proteins and compounds. However, such feature representation learning relies on a large-scale labelled dataset, which is not always available.Results: We present an end-to-end deep learning framework, ELECTRA-DTA, to predict the binding affinity of drug-target pairs. This framework incorporates an unsupervised learning mechanism to train two ELECTRA-based contextual embedding models, one for protein amino acids and the other for compound SMILES string encoding. In addition, ELECTRA-DTA leverages a squeeze-and-excitation (SE) convolutional neural network block stacked over three fully connected layers to further capture the sequential and spatial features of the protein sequence and SMILES for the DTA regression task. Experimental evaluations show that ELECTRA-DTA outperforms various state-of-the-art DTA prediction models, especially with the challenging, interaction-sparse BindingDB dataset. In target selection and drug repurposing for COVID-19, ELECTRA-DTA also offers competitive performance, suggesting its potential in speeding drug discovery and generalizability for other compound- or protein-related computational tasks.

scholarly journals DTI-SNNFRA: Drug-target interaction prediction by shared nearest neighbors and fuzzy-rough approximation

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246920
Author(s):  
Sk Mazharul Islam ◽  
Sk Md Mosaddek Hossain ◽  
Sumanta Ray
Keyword(s):  
Drug Discovery ◽  
Drug Target ◽  
Drug Repositioning ◽  
Geometric Mean ◽  
Imbalanced Data ◽  
Drug Repurposing ◽  
Search Space ◽  
Classification Model ◽  
Target Interaction ◽  
Rough Approximation

In-silico prediction of repurposable drugs is an effective drug discovery strategy that supplements de-nevo drug discovery from scratch. Reduced development time, less cost and absence of severe side effects are significant advantages of using drug repositioning. Most recent and most advanced artificial intelligence (AI) approaches have boosted drug repurposing in terms of throughput and accuracy enormously. However, with the growing number of drugs, targets and their massive interactions produce imbalanced data which may not be suitable as input to the classification model directly. Here, we have proposed DTI-SNNFRA, a framework for predicting drug-target interaction (DTI), based on shared nearest neighbour (SNN) and fuzzy-rough approximation (FRA). It uses sampling techniques to collectively reduce the vast search space covering the available drugs, targets and millions of interactions between them. DTI-SNNFRA operates in two stages: first, it uses SNN followed by a partitioning clustering for sampling the search space. Next, it computes the degree of fuzzy-rough approximations and proper degree threshold selection for the negative samples’ undersampling from all possible interaction pairs between drugs and targets obtained in the first stage. Finally, classification is performed using the positive and selected negative samples. We have evaluated the efficacy of DTI-SNNFRA using AUC (Area under ROC Curve), Geometric Mean, and F1 Score. The model performs exceptionally well with a high prediction score of 0.95 for ROC-AUC. The predicted drug-target interactions are validated through an existing drug-target database (Connectivity Map (Cmap)).

scholarly journals A learning-based method for drug-target interaction prediction based on feature representation learning and deep neural network

2020 ◽  
Vol 21 (S13) ◽  
Author(s):  
Jiajie Peng ◽  
Jingyi Li ◽  
Xuequn Shang
Keyword(s):  
Neural Network ◽  
Drug Discovery ◽  
Drug Target ◽  
Deep Neural Network ◽  
Computational Prediction ◽  
Representation Learning ◽  
Feature Representation ◽  
New Drugs ◽  
Target Interaction ◽  
Interaction Prediction

Abstract Background Drug-target interaction prediction is of great significance for narrowing down the scope of candidate medications, and thus is a vital step in drug discovery. Because of the particularity of biochemical experiments, the development of new drugs is not only costly, but also time-consuming. Therefore, the computational prediction of drug target interactions has become an essential way in the process of drug discovery, aiming to greatly reducing the experimental cost and time. Results We propose a learning-based method based on feature representation learning and deep neural network named DTI-CNN to predict the drug-target interactions. We first extract the relevant features of drugs and proteins from heterogeneous networks by using the Jaccard similarity coefficient and restart random walk model. Then, we adopt a denoising autoencoder model to reduce the dimension and identify the essential features. Third, based on the features obtained from last step, we constructed a convolutional neural network model to predict the interaction between drugs and proteins. The evaluation results show that the average AUROC score and AUPR score of DTI-CNN were 0.9416 and 0.9499, which obtains better performance than the other three existing state-of-the-art methods. Conclusions All the experimental results show that the performance of DTI-CNN is better than that of the three existing methods and the proposed method is appropriately designed.

scholarly journals TranDTA: Prediction Of Drug Target Binding Affinity Using Transformer Representations

2021 ◽  
Author(s):  
Mahsa Saadat ◽  
Armin Behjati ◽  
Fatemeh Zare-Mirakabad ◽  
Sajjad Gharaghani
Keyword(s):  
Drug Discovery ◽  
Binding Affinity ◽  
Drug Target ◽  
Feature Vector ◽  
Structural Information ◽  
Binary Classification ◽  
Drug Repurposing ◽  
Target Pair ◽  
Target Binding ◽  
State Of Art

Drug discovery is generally difficult, expensive and the success rate is low. One of the essential steps in the early stages of drug discovery and drug repurposing is identifying drug target interactions. Although several methods developed use binary classification to predict if the interaction between a drug and its target exists or not, it is more informative and challenging to predict the strength of the binding between a drug and its target. Binding affinity indicates the strength of drug-target pair interactions. In this regard, several computational methods have been developed to predict the drug-target binding affinity. With the advent of deep learning methods, the accuracy of binding affinity prediction is improving. However, the input representation of these models is very effective in the result. The early models only use the sequence of molecules and the latter models focus on the structure of them. Although the recent models predict binding affinity more accurate than the first ones, they need more data and resources for training. In this study, we present a method that uses a pre-trained transformer to represent the protein as model input. Although pretrained transformer extracts a feature vector of the protein sequence, they can learn structural information in layers and heads. So, the extracted feature vector by transformer includes the sequence and structural properties of protein. Therefore, our method can also be run without limitations on resources (memory, CPU and GPU). The results show that our model achieves a competitive performance with the state-of-art models. Data and trained model is available at http://bioinformatics.aut.ac.ir/TranDTA/ .

A Drug Target Interaction Prediction Based on LINE-RF Learning

2020 ◽  
Vol 15 (7) ◽  
pp. 750-757
Author(s):  
Jihong Wang ◽  
Yue Shi ◽  
Xiaodan Wang ◽  
Huiyou Chang
Keyword(s):  
Network Topology ◽  
Drug Target ◽  
Large Scale ◽  
Representation Learning ◽  
New Drugs ◽  
Combination Method ◽  
Learning Methods ◽  
Network Representation ◽  
On Line ◽  
Clinical Experiments

Background: At present, using computer methods to predict drug-target interactions (DTIs) is a very important step in the discovery of new drugs and drug relocation processes. The potential DTIs identified by machine learning methods can provide guidance in biochemical or clinical experiments. Objective: The goal of this article is to combine the latest network representation learning methods for drug-target prediction research, improve model prediction capabilities, and promote new drug development. Methods: We use large-scale information network embedding (LINE) method to extract network topology features of drugs, targets, diseases, etc., integrate features obtained from heterogeneous networks, construct binary classification samples, and use random forest (RF) method to predict DTIs. Results: The experiments in this paper compare the common classifiers of RF, LR, and SVM, as well as the typical network representation learning methods of LINE, Node2Vec, and DeepWalk. It can be seen that the combined method LINE-RF achieves the best results, reaching an AUC of 0.9349 and an AUPR of 0.9016. Conclusion: The learning method based on LINE network can effectively learn drugs, targets, diseases and other hidden features from the network topology. The combination of features learned through multiple networks can enhance the expression ability. RF is an effective method of supervised learning. Therefore, the Line-RF combination method is a widely applicable method.

scholarly journals A Novel Method to Predict Drug-Target Interactions Based on Large-Scale Graph Representation Learning

Cancers ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 2111
Author(s):  
Bo-Wei Zhao ◽  
Zhu-Hong You ◽  
Lun Hu ◽  
Zhen-Hao Guo ◽  
Lei Wang ◽  
...  
Keyword(s):  
Drug Target ◽  
Large Scale ◽  
Computational Models ◽  
Structural Information ◽  
Characteristic Curve ◽  
Representation Learning ◽  
Graph Representation ◽  
Convolutional Network ◽  
Novel Method

Identification of drug-target interactions (DTIs) is a significant step in the drug discovery or repositioning process. Compared with the time-consuming and labor-intensive in vivo experimental methods, the computational models can provide high-quality DTI candidates in an instant. In this study, we propose a novel method called LGDTI to predict DTIs based on large-scale graph representation learning. LGDTI can capture the local and global structural information of the graph. Specifically, the first-order neighbor information of nodes can be aggregated by the graph convolutional network (GCN); on the other hand, the high-order neighbor information of nodes can be learned by the graph embedding method called DeepWalk. Finally, the two kinds of feature are fed into the random forest classifier to train and predict potential DTIs. The results show that our method obtained area under the receiver operating characteristic curve (AUROC) of 0.9455 and area under the precision-recall curve (AUPR) of 0.9491 under 5-fold cross-validation. Moreover, we compare the presented method with some existing state-of-the-art methods. These results imply that LGDTI can efficiently and robustly capture undiscovered DTIs. Moreover, the proposed model is expected to bring new inspiration and provide novel perspectives to relevant researchers.

ACPred-Fuse: fusing multi-view information improves the prediction of anticancer peptides

2019 ◽  
Vol 21 (5) ◽  
pp. 1846-1855 ◽  
Author(s):  
Bing Rao ◽  
Chen Zhou ◽  
Guoying Zhang ◽  
Ran Su ◽  
Leyi Wei
Keyword(s):  
Anticancer Activity ◽  
Large Scale ◽  
Representation Learning ◽  
Feature Representation ◽  
Accurate Identification ◽  
Discriminative Ability ◽  
Anticancer Peptides ◽  
Probabilistic Information ◽  
Feature Descriptors ◽  
Comparison Results

Abstract Fast and accurate identification of the peptides with anticancer activity potential from large-scale proteins is currently a challenging task. In this study, we propose a new machine learning predictor, namely, ACPred-Fuse, that can automatically and accurately predict protein sequences with or without anticancer activity in peptide form. Specifically, we establish a feature representation learning model that can explore class and probabilistic information embedded in anticancer peptides (ACPs) by integrating a total of 29 different sequence-based feature descriptors. In order to make full use of various multiview information, we further fused the class and probabilistic features with handcrafted sequential features and then optimized the representation ability of the multiview features, which are ultimately used as input for training our prediction model. By comparing the multiview features and existing feature descriptors, we demonstrate that the fused multiview features have more discriminative ability to capture the characteristics of ACPs. In addition, the information from different views is complementary for the performance improvement. Finally, our benchmarking comparison results showed that the proposed ACPred-Fuse is more precise and promising in the identification of ACPs than existing predictors. To facilitate the use of the proposed predictor, we built a web server, which is now freely available via http://server.malab.cn/ACPred-Fuse.

scholarly journals Causal network models of SARS-CoV-2 expression and aging to identify candidates for drug repurposing

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Anastasiya Belyaeva ◽  
Louis Cammarata ◽  
Adityanarayanan Radhakrishnan ◽  
Chandler Squires ◽  
Karren Dai Yang ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Tyrosine Kinases ◽  
Large Scale ◽  
Drug Repurposing ◽  
Network Models ◽  
Structural Data ◽  
Multiple Data ◽  
Approved Drugs ◽  
Causal Framework

AbstractGiven the severity of the SARS-CoV-2 pandemic, a major challenge is to rapidly repurpose existing approved drugs for clinical interventions. While a number of data-driven and experimental approaches have been suggested in the context of drug repurposing, a platform that systematically integrates available transcriptomic, proteomic and structural data is missing. More importantly, given that SARS-CoV-2 pathogenicity is highly age-dependent, it is critical to integrate aging signatures into drug discovery platforms. We here take advantage of large-scale transcriptional drug screens combined with RNA-seq data of the lung epithelium with SARS-CoV-2 infection as well as the aging lung. To identify robust druggable protein targets, we propose a principled causal framework that makes use of multiple data modalities. Our analysis highlights the importance of serine/threonine and tyrosine kinases as potential targets that intersect the SARS-CoV-2 and aging pathways. By integrating transcriptomic, proteomic and structural data that is available for many diseases, our drug discovery platform is broadly applicable. Rigorous in vitro experiments as well as clinical trials are needed to validate the identified candidate drugs.

scholarly journals A Machine Learning-Enabled Pipeline for Large-Scale Virtual Drug Screening

2021 ◽  
Author(s):  
Aayush Gupta ◽  
Huan-Xiang Zhou
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Machine Learning ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Drug Screening ◽  
Drug Target ◽  
Large Scale ◽  
False Positives ◽  
Virtual Drug Screening

Virtual screening is receiving renewed attention in drug discovery, but progress is hampered by challenges on two fronts: handling the ever increasing sizes of libraries of drug-like compounds, and separating true positives from false positives. Here we developed a machine learning-enabled pipeline for large-scale virtual screening that promises breakthroughs on both fronts. By clustering compounds according to molecular properties and limited docking against a drug target, the full library was trimmed by 10-fold; the remaining compounds were then screened individually by docking; and finally a dense neural network was trained to classify the hits into true and false positives. As illustration, we screened for inhibitors against RPN11, the deubiquitinase subunit of the proteasome and a drug target for breast cancer.

scholarly journals Scalable Person Re-Identification by Harmonious Attention

2019 ◽  
Vol 128 (6) ◽  
pp. 1635-1653 ◽  
Author(s):  
Wei Li ◽  
Xiatian Zhu ◽  
Shaogang Gong
Keyword(s):  
Large Scale ◽  
Feature Matching ◽  
Representation Learning ◽  
Video Data ◽  
Feature Representation ◽  
Surveillance Video ◽  
Deep Feature ◽  
Benchmark Datasets ◽  
Attention Selection ◽  
The Cost

AbstractExisting person re-identification (re-id) deep learning methods rely heavily on the utilisation of large and computationally expensive convolutional neural networks. They are therefore not scalable to large scale re-id deployment scenarios with the need of processing a large amount of surveillance video data, due to the lengthy inference process with high computing costs. In this work, we address this limitation via jointly learning re-id attention selection. Specifically, we formulate a novel harmonious attention network (HAN) framework to jointly learn soft pixel attention and hard region attention alongside simultaneous deep feature representation learning, particularly enabling more discriminative re-id matching by efficient networks with more scalable model inference and feature matching. Extensive evaluations validate the cost-effectiveness superiority of the proposed HAN approach for person re-id against a wide variety of state-of-the-art methods on four large benchmark datasets: CUHK03, Market-1501, DukeMTMC, and MSMT17.

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