Similarity-based machine learning methods for predicting drug-target interactions: a brief review

By Hao Ding, Ichigaku Takigawa, Hiroshi Mamitsuka, Shanfeng Zhu. Briefings Bioinform., 2014

384 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/fc8b3a7622ddf5ab88fe06de260898202cf06233

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Citations in the corpus

Semantic Matching in Search (2014, background): “These include collaborative filtering [101, 1, 2, 102, 45, 149, 9, 44], image annotation [83, 76, 159], paraphrasing and textual entailment [62, 54, 139, 203, 55, 85, 165, 35], question answering [37, 126, 21, 205, 178, 130], cross-language information retrieval [64, 141, 140], short text conversati”
Toward more realistic drug–target interaction predictions (2014, neutral): “For the target sequence similarities, we tried out both the original and normalized versions of the Smith–Waterman (SW) score [8, 13].”
Towards Personalized Medicine: Leveraging Patient Similarity and Drug Similarity Analytics (2014, background): “This approach has been shown to deliver competitive or even better accuracy to more complex, feature-vector-based methods 9, 11 (e.g., support vector machines, random forests).”
SwissTargetPrediction: updated data and new features for efficient prediction of protein targets of small molecules (2019, background): “Ligand-based target prediction has proven highly perfor-mant and fast in predicting correct protein targets of compounds in drug discovery contexts (4,5).”
DeepDock: Enhancing Ligand-protein Interaction Prediction by a Combination of Ligand and Structure Information (2019, positive): “Thus ligand-based approaches have been widely adopted [7].”
Interpretable Drug Target Prediction Using Deep Neural Representation (2018, methodology): “…descriptors [Faulon et al. , 2007]; (2) an aggregation of expert designed similarity measures to derive candidate pairs from known DTI pairs [Ding et al. , 2013; Yamanishi et al. , 2010]; and (3) representations learned from descriptors from (1), for example, using techniques such as…”
SimBoost: a read-across approach for predicting drug–target binding affinities using gradient boosting machines (2017, background): “Recent studies [1] have demonstrated that machine learning-based approaches have the potential to predict compound-protein interactions on a large scale by learning from limited interaction data supplemented with information on the similarity among compounds and among proteins.”
Neighborhood Regularized Logistic Matrix Factorization for Drug-Target Interaction Prediction (2016, methodology): “Following similar settings in previous studies [12, 14, 15, 19, 24], four well-known biological databases, i.”
Compound‐protein interaction prediction with end‐to‐end learning of neural networks for graphs and sequences (2018, methodology): “Based on this observation, screening true negative samples is important to create highly credible CPI datasets (Ding et al., 2014). First, we experimented with two sets of CPI datasets, for human and C.elegans, recently created by Liu et al. (2015). The datasets include highly credible negative samp”