MPSM-DTI: prediction of drug–target interaction <i>via</i> machine learning based on the chemical structure and protein sequence

Peng, Yayuan; Wang, Jiye; Wu, Zengrui; Zheng, Lulu; Wang, Biting; Liu, Guixia; Li, Weihua; Tang, Yun

doi:10.1039/d1dd00011j

Cited by 10 publications

(6 citation statements)

References 27 publications

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“…This model combines DDA_Jac, DDI_Cos, and Structures as an integrated drug similarity using SNF and employs Seq_Loc as a target similarity. To verify that the model selected by FSI is the best, we compared its performance to those of the models with full similarity integration, random similarity integrations, and the original heterogeneous network (OHN) model, which uses only Structures and Seq_Loc, proposed by Liu et al (2022) , Liu et al (2016) , Peng et al (2022) and Wang, Yang & Li (2013a) . To demonstrate the efficiency of FSI, we first compare the performance of the FSI model with that of the full model which fuses all drug similarities and target similarities, as shown in Fig.…”

Section: Resultsmentioning

confidence: 99%

“…To predict DTIs, the network models utilize the drug–drug similarity derived only from drug chemical structures and the target–target similarity based only on local sequence alignments of target proteins. To show the superior performance of the model optimally integrating multiple drug and target similarities by FSI, we compared the performances of the OHN model ( Liu et al, 2022 ; Liu et al, 2016 ; Peng et al, 2022 ; Wang, Yang & Li, 2013a ) with those of the FSI model ( Fig. 7 ).…”

Section: Resultsmentioning

confidence: 99%

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Heterogeneous network propagation with forward similarity integration to enhance drug–target association prediction

Tangmanussukum

Kawichai²,

Suratanee

et al. 2022

PeerJ Computer Science

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Identification of drug–target interaction (DTI) is a crucial step to reduce time and cost in the drug discovery and development process. Since various biological data are publicly available, DTIs have been identified computationally. To predict DTIs, most existing methods focus on a single similarity measure of drugs and target proteins, whereas some recent methods integrate a particular set of drug and target similarity measures by a single integration function. Therefore, many DTIs are still missing. In this study, we propose heterogeneous network propagation with the forward similarity integration (FSI) algorithm, which systematically selects the optimal integration of multiple similarity measures of drugs and target proteins. Seven drug–drug and nine target–target similarity measures are applied with four distinct integration methods to finally create an optimal heterogeneous network model. Consequently, the optimal model uses the target similarity based on protein sequences and the fused drug similarity, which combines the similarity measures based on chemical structures, the Jaccard scores of drug–disease associations, and the cosine scores of drug–drug interactions. With an accuracy of 99.8%, this model significantly outperforms others that utilize different similarity measures of drugs and target proteins. In addition, the validation of the DTI predictions of this model demonstrates the ability of our method to discover missing potential DTIs.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Heterogeneous network propagation with forward similarity integration to enhance drug–target association prediction

Tangmanussukum

Kawichai²,

Suratanee

et al. 2022

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…In recent years, machine learning has emerged as a major challenger to classical ligandand structure-based methods [52][53][54]. Many machine learning models such as random forest, support vector machine, or naive bayes have been developed to predict drug-target interactions [55][56][57]. With increasing computational power, deep learning models have become more popular.…”

Section: Machine Learning-based Methodsmentioning

confidence: 99%

PanScreen: A Comprehensive Approach to Off-Target Liability Assessment

Sellner,

Lill,

Smieško

2023

Preprint

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Drug development projects are getting increasingly more expensive while their success rate is stagnating. Safety issues attributed to off-target binding represent a major reason for the failure of new drugs. Besides desired on-target binding, small molecules may interact with off-targets, triggering adverse effects. Therefore, the development of novel methods for early recognition of such issues that are resource-efficient and cost-effective becomes vital. Here, we introduce PanScreen, an online platform for the automated assessment of off-target liabilities. PanScreen combines structure-based modeling techniques with state-of-the-art deep learning methods to not only predict accurate binding affinities but also give insight into potential modes of action. We show that the predictions are approaching experimental accuracy found in public datasets and that the same technology can also be used for other research areas, such as drug repurposing. Such fast and inexpensive methods allow researchers to test not only drug candidates, but all small molecules that might come into contact with a human organism for potential safety concerns very early in the development process. PanScreen is publicly available atwww.panscreen.ch.

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“…Hence, the EDC-Predictor could predict other targets outside of NRs for EDCs. According to a previous study, 51 we used the top 50 as the threshold to analyze the NBTP part of the CTPs generated for 14 EDCs. Nine of them were reported to target one or more of the proteins in the steroid hormone biosynthesis pathway, including CYP19, 17β-HSD, CYP11B1, and CYP11B2.…”

Section: Construction Of Edc Prediction Models Using Various Types Of...mentioning

confidence: 99%

EDC-Predictor: A Novel Strategy for Prediction of Endocrine-Disrupting Chemicals by Integrating Pharmacological and Toxicological Profiles

Zhou

et al. 2023

Environ. Sci. Technol.

Self Cite

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Identification of endocrine-disrupting chemicals (EDCs) is crucial in the reduction of human health risks. However, it is hard to do so because of the complex mechanisms of the EDCs. In this study, we propose a novel strategy named EDC-Predictor to integrate pharmacological and toxicological profiles for the prediction of EDCs. Different from conventional methods that only focus on a few nuclear receptors (NRs), EDC-Predictor considers more targets. It uses computational target profiles from network-based and machine learning-based methods to characterize compounds, including both EDCs and non-EDCs. The best model constructed by these target profiles outperformed those models by molecular fingerprints. In a case study to predict NR-related EDCs, EDC-Predictor showed a wider applicability domain and higher accuracy than four previous tools. Another case study further demonstrated that EDC-Predictor could predict EDCs targeting other proteins rather than NRs. Finally, a free web server was developed to make EDC prediction easier (). In summary, EDC-Predictor would be a powerful tool in EDC prediction and drug safety assessment.

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MPSM-DTI: prediction of drug–target interaction via machine learning based on the chemical structure and protein sequence

Abstract: Drug-target interaction (DTI) plays a central role in drug discovery. How to predict DTI quickly and accurately is a key issue. Traditional structure-based and ligand-based methods have some inherent deficiencies....

Cited by 10 publications

References 27 publications

Heterogeneous network propagation with forward similarity integration to enhance drug–target association prediction

Heterogeneous network propagation with forward similarity integration to enhance drug–target association prediction

PanScreen: A Comprehensive Approach to Off-Target Liability Assessment

EDC-Predictor: A Novel Strategy for Prediction of Endocrine-Disrupting Chemicals by Integrating Pharmacological and Toxicological Profiles

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