A comparative study of SMILES-based compound similarity functions for drug-target interaction prediction

Öztürk, Hakime; Özkırımlı, Elif; Özgür, Arzucan

doi:10.1186/s12859-016-0977-x

Cited by 125 publications

(99 citation statements)

References 43 publications

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“…As the field of drug discovery expands with the discovery of new drugs, repurposing of existing drugs and identification of novel interacting partners for approved drugs is also gaining interest ( Oprea and Mestres, 2012 ). Until recently, DTI prediction was approached as a binary classification problem ( Bleakley and Yamanishi, 2009 ; Cao et al ., 2014 , 2012 ; Cobanoglu et al ., 2013 ; Gönen, 2012 ; Öztürk et al ., 2016 ; Yamanishi et al ., 2008 ; van Laarhoven et al ., 2011 ), neglecting an important piece of information about protein–ligand interactions, namely the binding affinity values. Binding affinity provides information on the strength of the interaction between a drug–target (DT) pair and it is usually expressed in measures such as dissociation constant (K d ), inhibition constant (K i ) or the half maximal inhibitory concentration (IC 50 ).…”

Section: Introductionmentioning

confidence: 99%

DeepDTA: deep drug–target binding affinity prediction

2018

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MotivationThe identification of novel drug–target (DT) interactions is a substantial part of the drug discovery process. Most of the computational methods that have been proposed to predict DT interactions have focused on binary classification, where the goal is to determine whether a DT pair interacts or not. However, protein–ligand interactions assume a continuum of binding strength values, also called binding affinity and predicting this value still remains a challenge. The increase in the affinity data available in DT knowledge-bases allows the use of advanced learning techniques such as deep learning architectures in the prediction of binding affinities. In this study, we propose a deep-learning based model that uses only sequence information of both targets and drugs to predict DT interaction binding affinities. The few studies that focus on DT binding affinity prediction use either 3D structures of protein–ligand complexes or 2D features of compounds. One novel approach used in this work is the modeling of protein sequences and compound 1D representations with convolutional neural networks (CNNs).ResultsThe results show that the proposed deep learning based model that uses the 1D representations of targets and drugs is an effective approach for drug target binding affinity prediction. The model in which high-level representations of a drug and a target are constructed via CNNs achieved the best Concordance Index (CI) performance in one of our larger benchmark datasets, outperforming the KronRLS algorithm and SimBoost, a state-of-the-art method for DT binding affinity prediction.Availability and implementation https://github.com/hkmztrk/DeepDTA Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Introductionmentioning

confidence: 99%

DeepDTA: deep drug–target binding affinity prediction

2018

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“…The values of the elements in the association hypergraph are the similarity measurements between the corresponding elements. For similarity among the vertices, we used a compound (metabolites) similarity score for which numerous metrics are available 40 , 41 . Specifically, we selected the similarity score calculated by ChemMine tools, which have an R interface to recognize the CID number of the compounds 42 .…”

Section: Methodsmentioning

confidence: 99%

A genome-scale metabolic network alignment method within a hypergraph-based framework using a rotational tensor-vector product

Shen

Zhang

Chen

et al. 2018

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Biological network alignment aims to discover important similarities and differences and thus find a mapping between topological and/or functional components of different biological molecular networks. Then, the mapped components can be considered to correspond to both their places in the network topology and their biological attributes. Development and evolution of biological network alignment methods has been accelerated by the rapidly increasing availability of such biological networks, yielding a repertoire of tens of methods based upon graph theory. However, most biological processes, especially the metabolic reactions, are more sophisticated than simple pairwise interactions and contain three or more participating components. Such multi-lateral relations are not captured by graphs, and computational methods to overcome this limitation are currently lacking. This paper introduces hypergraphs and association hypergraphs to describe metabolic networks and their potential alignments, respectively. Within this framework, metabolic networks are aligned by identifying the maximal Z-eigenvalue of a symmetric tensor. A shifted higher-order power method was utilized to identify a solution. A rotational strategy has been introduced to accelerate the tensor-vector product by 250-fold on average and reduce the storage cost by up to 1,000-fold. The algorithm was implemented on a spark-based distributed computation cluster to significantly increase the convergence rate further by 50- to 80-fold. The parameters have been explored to understand their impact on alignment accuracy and speed. In particular, the influence of initial value selection on the stationary point has been simulated to ensure an accurate approximation of the global optimum. This framework was demonstrated by alignments among the genome-wide metabolic networks of Escherichia coli MG-1655 and Halophilic archaeon DL31. To our knowledge, this is the first genome-wide metabolic network alignment at both the metabolite level and the enzyme level. These results demonstrate that it can supply quite a few valuable insights into metabolic networks. First, this method can access the driving force of organic reactions through the chemical evolution of metabolic network. Second, this method can incorporate the chemical information of enzymes and structural changes of compounds to offer new way defining reaction class and module, such as those in KEGG. Third, as a vertex-focused treatment, this method can supply novel structural and functional annotation for ill-defined molecules. The related source code is available on request.

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“…Protein sequences are usually stored in the form of letters, in which the number of letters is 20, representing 20 amino acids. In order to facilitate the processing of www.nature.com/scientificreports www.nature.com/scientificreports/ machine learning algorithm, we use Position-Specific Scoring Matrix (PSSM) to transform it into a numerical matrix 33,34 . The advantage of this strategy is that it can extract the biological evolutionary information carried in the protein sequence, which is conducive to deep mining.…”

Section: Drug Molecular Characterization Studies Show That Molecularmentioning

confidence: 99%

Incorporating chemical sub-structures and protein evolutionary information for inferring drug-target interactions

Wang

You

et al. 2020

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Accumulating evidence has shown that drug-target interactions (Dtis) play a crucial role in the process of genomic drug discovery. Although biological experimental technology has made great progress, the identification of DTIs is still very time-consuming and expensive nowadays. Hence it is urgent to develop in silico model as a supplement to the biological experiments to predict the potential Dtis. In this work, a new model is designed to predict DTIs by incorporating chemical sub-structures and protein evolutionary information. Specifically, we first use Position-Specific Scoring Matrix (PSSM) to convert the protein sequence into the numerical descriptor containing biological evolutionary information, then use Discrete Cosine Transform (DCT) algorithm to extract the hidden features and integrate them with the chemical sub-structures descriptor, and finally utilize Rotation Forest (RF) classifier to accurately predict whether there is interaction between the drug and the target protein. In the 5-fold cross-validation (CV) experiment, the average accuracy of the proposed model on the benchmark datasets of Enzymes, Ion Channels, GPCRs and Nuclear Receptors reached 0.9140, 0.8919, 0.8724 and 0.8111, respectively. In order to fully evaluate the performance of the proposed model, we compare it with different feature extraction model, classifier model, and other state-of-the-art models. Furthermore, we also implemented case studies. As a result, 8 of the top 10 drug-target pairs with the highest prediction score were confirmed by related databases. These excellent results indicate that the proposed model has outstanding ability in predicting Dtis and can provide reliable candidates for biological experiments. Drugs can regulate the physiological function of the human body, to provide guarantee for disease prevention, treatment and other aspects. More importantly, the discovery and identification of drug targets is the source of drug research, which plays a key role in the success of drug development. The complexity of the etiologies of most diseases leading to disease-related genes or proteins may be potential drug targets, but because of target specificity, robustness of biological networks and other factors, the number of newly developed drugs does not rapidly increase with the development of proteomics and chemical genomics. So far, only a small number of targets in the human genome, in which the total number of pharmacological interest is about 6000 to 8000, have been confirmed to be associated with approved drugs 1-4. As the experiment-based method having the disadvantage of high cost, time consuming and limitations of small-scale in identifying drug-target interactions, researchers try to mine drug-related targets in the whole genome using computational-based methods 5-11. At present, researchers have designed many computational-based models to analyze and predict drug-target interactions (DTIs) 12-18. For example, Yamanishi et al. designed a model based on statistical algorithm to predict potential DTIs, which ...

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A comparative study of SMILES-based compound similarity functions for drug-target interaction prediction

Cited by 125 publications

References 43 publications

DeepDTA: deep drug–target binding affinity prediction

DeepDTA: deep drug–target binding affinity prediction

A genome-scale metabolic network alignment method within a hypergraph-based framework using a rotational tensor-vector product

Incorporating chemical sub-structures and protein evolutionary information for inferring drug-target interactions

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