Erratum to: 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/bbaa020

Cited by 4 publications

(2 citation statements)

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“…K-Nearest Neighbors (kNN) kNN defines a predicted category of an unknown sample based on the K closest data values in a training set [32]. Fuzzy kNN classification method was utilized to analyze drug compound data based on a 2D fingerprint via G protein-coupled receptors [33]. Naïve Bayesian Classifier (NBC) NBC calculates the set of probabilities by counting the frequency of categories for the feature to be predicted in the data [34].…”

Section: Classification Penalized Logistic Regressionmentioning

confidence: 99%

Big data and artificial intelligence (AI) methodologies for computer-aided drug design (CADD)

Lee

Maria-Solano

et al. 2022

Biochemical Society Transactions

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There have been numerous advances in the development of computational and statistical methods and applications of big data and artificial intelligence (AI) techniques for computer-aided drug design (CADD). Drug design is a costly and laborious process considering the biological complexity of diseases. To effectively and efficiently design and develop a new drug, CADD can be used to apply cutting-edge techniques to various limitations in the drug design field. Data pre-processing approaches, which clean the raw data for consistent and reproducible applications of big data and AI methods are introduced. We include the current status of the applicability of big data and AI methods to drug design areas such as the identification of binding sites in target proteins, structure-based virtual screening (SBVS), and absorption, distribution, metabolism, excretion and toxicity (ADMET) property prediction. Data pre-processing and applications of big data and AI methods enable the accurate and comprehensive analysis of massive biomedical data and the development of predictive models in the field of drug design. Understanding and analyzing biological, chemical, or pharmaceutical architectures of biomedical entities related to drug design will provide beneficial information in the biomedical big data era.

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Section: Classification Penalized Logistic Regressionmentioning

confidence: 99%

Big data and artificial intelligence (AI) methodologies for computer-aided drug design (CADD)

Lee

Maria-Solano

et al. 2022

Biochemical Society Transactions

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“…Instead of an exhaustive in vitro search, in silico methods start with candidate selection followed by experimental confirmation. 2 As suggested by a recent review article, 5 a brief categorization of the existing in silico methods is summarized in Figure 1.…”

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confidence: 99%

ML-DTI: Mutual Learning Mechanism for Interpretable Drug–Target Interaction Prediction

Yang

Zhong

Zhao

et al. 2021

J. Phys. Chem. Lett.

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Deep learning (DL) provides opportunities for the identification of drug–target interactions (DTIs). The challenges of applying DL lie primarily with the lack of interpretability. Also, most of the existing DL-based methods formulate the drug and target encoder as two independent modules without considering the relationship between them. In this study, we propose a mutual learning mechanism to bridge the gap between the two encoders. We formulated the DTI problem from a global perspective by inserting mutual learning layers between the two encoders. The mutual learning layer was achieved by multihead attention and position-aware attention. The neural attention mechanism also provides effective visualization, which makes it easier to analyze a model. We evaluated our approach using three benchmark kinase data sets under different experimental settings and compared the proposed method to three baseline models. We found that the four methods yielded similar results in the random split setting (training and test sets share common drugs and targets), while the proposed method increases the predictive performance significantly in the orphan-target and orphan-drug split setting (training and test sets share only targets or drugs). The experimental results demonstrated that the proposed method improved the generalization and interpretation capability of DTI modeling.

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ERT-GFAN: A multimodal drug–target interaction prediction model based on molecular biology and knowledge-enhanced attention mechanism

Cheng,

Yang,

Guan

et al. 2024

Computers in Biology and Medicine

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

Cited by 4 publications

References 0 publications

Big data and artificial intelligence (AI) methodologies for computer-aided drug design (CADD)

Big data and artificial intelligence (AI) methodologies for computer-aided drug design (CADD)

ML-DTI: Mutual Learning Mechanism for Interpretable Drug–Target Interaction Prediction

ERT-GFAN: A multimodal drug–target interaction prediction model based on molecular biology and knowledge-enhanced attention mechanism

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