Machine learning approaches and databases for prediction of drug–target interaction: a survey paper

Bagherian, Maryam; Sabeti, Elyas; Wang, Kai; Sartor, Maureen A.; Nikolovska‐Coleska, Zaneta; Najarian, Kayvan

doi:10.1093/bib/bbz157

Cited by 253 publications

(175 citation statements)

References 295 publications

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“…The potential of machine learning models to predict the binding affinity between new drug-target pairs has been demonstrated in various studies. Bagherian et al (44) briefly reviewed drug-target interaction prediction by machine learning models. Recently, machine learning methods have been used to search for cures for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (45-47), which has given direction for the promotion of new drug discovery.…”

Section: Figurementioning

confidence: 99%

Identification of drug compounds for keloids and hypertrophic scars: drug discovery based on text mining and DeepPurpose

Pan¹,

Chen²,

Qi³

et al. 2021

Ann Transl Med

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Background: Keloids (KL) and hypertrophic scars (HS) are forms of abnormal cutaneous scarring characterized by excessive deposition of extracellular matrix and fibroblast proliferation. Currently, the efficacy of drug therapies for KL and HS is limited. The present study aimed to investigate new drug therapies for KL and HS by using computational methods.Methods: Text mining and GeneCodis were used to mine genes closely related to KL and HS. Proteinprotein interaction analysis was performed using Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and Cytoscape. The selection of drugs targeting the genes closely related to KL and HS was carried out using Pharmaprojects. Drug-target interaction prediction was performed using DeepPurpose, through which candidate drugs with the highest predicted binding affinity were finally obtained.Results: Our analysis using text mining identified 69 KL-and HS-related genes. Gene enrichment analysis generated 25 genes, representing 7 pathways and 130 targeting drugs. DeepPurpose recommended 14 drugs as the final drug list, including 2 phosphatidylinositol-4,5-bisphosphate 3-kinase (PI3K) inhibitors, 10 prostaglandin-endoperoxide synthase 2 (PTGS2) inhibitors and 2 vascular endothelial growth factor A (VEGFA) antagonists.Conclusions: Drug discovery using in silico text mining and DeepPurpose may be a powerful and effective way to identify drugs targeting the genes related to KL and HS.

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Section: Figurementioning

confidence: 99%

Identification of drug compounds for keloids and hypertrophic scars: drug discovery based on text mining and DeepPurpose

Pan¹,

Chen²,

Qi³

et al. 2021

Ann Transl Med

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“…However, mechanisms can still be tested using gene manipulation techniques such as CRISPR, non-coding RNAs, and/or gene overexpression to prove the concepts, encouraging researchers and pharmaceutical companies to design therapeutics for these candidate targets. Rapid growing in the field of computational drug discovery, artificial intelligence, and big pharmacogenomic/proteomic databases and tools [107][108][109][110][111][112][113][114][115] to predict molecular targets, mechanisms of action, drug responses and adverse effects will eventually benefit the pipeline of the master regulator-targeted immunotherapeutic strategies, even though rounds of extensive benchmarking and testing in vitro and in vivo are needed before full potentials of in silico approaches can be unleashed in clinical settings. Implementation of the user-friendly, web-based programs brings a huge opportunity to scientists and clinicians knowledgeable in biology and disease-specific contexts but have less-to-no coding skills.…”

Section: Prospects On Therapeutic Targeting Master Regulators Of Icsmentioning

confidence: 99%

Transcriptional Regulation of Cancer Immune Checkpoints: Emerging Strategies for Immunotherapy

et al. 2020

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The study of immune evasion has gained a well-deserved eminence in cancer research by successfully developing a new class of therapeutics, immune checkpoint inhibitors, such as pembrolizumab and nivolumab, anti-PD-1 antibodies. By aiming at the immune checkpoint blockade (ICB), these new therapeutics have advanced cancer treatment with notable increases in overall survival and tumor remission. However, recent reports reveal that 40–60% of patients fail to benefit from ICB therapy due to acquired resistance or tumor relapse. This resistance may stem from increased expression of co-inhibitory immune checkpoints or alterations in the tumor microenvironment that promotes immune suppression. Because these mechanisms are poorly elucidated, the transcription factors that regulate immune checkpoints, known as “master regulators”, have garnered interest. These include AP-1, IRF-1, MYC, and STAT3, which are known to regulate PD/PD-L1 and CTLA-4. Identifying these and other potential master regulators as putative therapeutic targets or biomarkers can be facilitated by mining cancer literature, public datasets, and cancer genomics resources. In this review, we describe recent advances in master regulator identification and characterization of the mechanisms underlying immune checkpoints regulation, and discuss how these master regulators of immune checkpoint molecular expression can be targeted as a form of auxiliary therapeutic strategy to complement traditional immunotherapy.

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“…Different ML methods have recently been applied to predict DTIs based on the various types of datasets [ 15 , 16 ]. The computational drug–target methods can be divided into three groups: ligand-based methods [ 17 , 18 ], docking-based methods [ 19 , 20 ] and chemogenomic methods [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

PreDTIs: prediction of drug–target interactions based on multiple feature information using gradient boosting framework with data balancing and feature selection techniques

Mahmud

Chen

Liu

et al. 2021

Briefings in Bioinformatics

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Discovering drug–target (protein) interactions (DTIs) is of great significance for researching and developing novel drugs, having a tremendous advantage to pharmaceutical industries and patients. However, the prediction of DTIs using wet-lab experimental methods is generally expensive and time-consuming. Therefore, different machine learning-based methods have been developed for this purpose, but there are still substantial unknown interactions needed to discover. Furthermore, data imbalance and feature dimensionality problems are a critical challenge in drug-target datasets, which can decrease the classifier performances that have not been significantly addressed yet. This paper proposed a novel drug–target interaction prediction method called PreDTIs. First, the feature vectors of the protein sequence are extracted by the pseudo-position-specific scoring matrix (PsePSSM), dipeptide composition (DC) and pseudo amino acid composition (PseAAC); and the drug is encoded with MACCS substructure fingerings. Besides, we propose a FastUS algorithm to handle the class imbalance problem and also develop a MoIFS algorithm to remove the irrelevant and redundant features for getting the best optimal features. Finally, balanced and optimal features are provided to the LightGBM Classifier to identify DTIs, and the 5-fold CV validation test method was applied to evaluate the prediction ability of the proposed method. Prediction results indicate that the proposed model PreDTIs is significantly superior to other existing methods in predicting DTIs, and our model could be used to discover new drugs for unknown disorders or infections, such as for the coronavirus disease 2019 using existing drugs compounds and severe acute respiratory syndrome coronavirus 2 protein sequences.

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Machine learning approaches and databases for prediction of drug–target interaction: a survey paper

Cited by 253 publications

References 295 publications

Identification of drug compounds for keloids and hypertrophic scars: drug discovery based on text mining and DeepPurpose

Identification of drug compounds for keloids and hypertrophic scars: drug discovery based on text mining and DeepPurpose

Transcriptional Regulation of Cancer Immune Checkpoints: Emerging Strategies for Immunotherapy

PreDTIs: prediction of drug–target interactions based on multiple feature information using gradient boosting framework with data balancing and feature selection techniques

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