Machine learning approaches and databases for prediction of drug–target interaction: a survey paper (2020-01-17T00:00:00.000000Z)

TL;DR

The data required for the task of DTI prediction is described followed by a comprehensive catalog consisting of machine learning methods and databases, which have been proposed and utilized to predict DTIs.

Abstract

Abstract The task of predicting the interactions between drugs and targets plays a key role in the process of drug discovery. There is a need to develop novel and efficient prediction approaches in order to avoid costly and laborious yet not-always-deterministic experiments to determine drug–target interactions (DTIs) by experiments alone. These approaches should be capable of identifying the potential DTIs in a timely manner. In this article, we describe the data required for the task of DTI prediction followed by a comprehensive catalog consisting of machine learning methods and databases, which have been proposed and utilized to predict DTIs. The advantages and disadvantages of each set of methods are also briefly discussed. Lastly, the challenges one may face in prediction of DTI using machine learning approaches are highlighted and we conclude by shedding some lights on important future research directions.

Authors

M. Bagherian

1 papers

Elyas Sabeti

1 papers

Kai Wang

1 papers

TL;DR

Abstract

Authors

References315 items

Combining

Predicting drug-target interactions using Lasso with random forest based on evolutionary information and chemical structure.

Applications of Machine Learning in Drug Discovery

Towards Better Drug Repositioning: Targeted Immunoinflammatory Therapy for Diabetic Nephropathy.

Drug Target Prediction by Multi-View Low Rank Embedding

Open-source chemogenomic data-driven algorithms for predicting drug-target interactions

Computational prediction of drug-target interactions using chemogenomic approaches: an empirical survey

Predicting drug-target interaction network using deep learning model

A comprehensive review of feature based methods for drug target interaction prediction

Recent Advances in the Machine Learning-Based Drug-Target Interaction Prediction.

NRLMFβ: Beta-distribution-rescored neighborhood regularized logistic matrix factorization for improving the performance of drug–target interaction prediction

PubChem and ChEMBL beyond Lipinski

Identification of drug-side effect association via multiple information integration with centered kernel alignment

Computational Prediction of Drug-Target Interactions via Ensemble Learning.

Drug-Target Interaction Prediction via Dual Laplacian Graph Regularized Matrix Completion

CoDe-DTI: Collaborative Deep Learning-based Drug-Target Interaction Prediction

DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences

BRENDA in 2019: a European ELIXIR core data resource

Survey of Similarity-based Prediction of Drug-protein Interactions.

DrugCentral 2018: an update

Drug-Target Interaction prediction using Multi Graph Regularized Nuclear Norm Minimization

Predicting Drug-Target Interaction Using Deep Matrix Factorization

BE-DTI': Ensemble framework for drug target interaction prediction using dimensionality reduction and active learning

Drug-Target Interaction Prediction with Weighted Bayesian Ranking

Deep learning-based transcriptome data classification for drug-target interaction prediction

Machine Learning for Drug-Target Interaction Prediction

Predicting Drug-Target on Heterogeneous Network with Co-rank

Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases

Netpredictor: R and Shiny package to perform drug-target network analysis and prediction of missing links

An integrative approach to develop computational pipeline for drug-target interaction network analysis

Multi-view Visual Bayesian Personalized Ranking from Implicit Feedback

Interpretable Drug Target Prediction Using Deep Neural Representation

Linking drug target and pathway activation for effective therapy using multi-task learning

A Systematic Prediction of Drug-Target Interactions Using Molecular Fingerprints and Protein Sequences.

NeoDTI: Neural integration of neighbor information from a heterogeneous network for discovering new drug-target interactions

DeepDTA: deep drug–target binding affinity prediction

Drug-target interaction prediction: A Bayesian ranking approach

Identification of drug-target interactions via multiple information integration

Drug-Target Interaction Prediction through Label Propagation with Linear Neighborhood Information

DDR: efficient computational method to predict drug–target interactions using graph mining and machine learning approaches

Therapeutic target database update 2018: enriched resource for facilitating bench-to-clinic research of targeted therapeutics

ECOdrug: a database connecting drugs and conservation of their targets across species

SuperDRUG2: a one stop resource for approved/marketed drugs

DrugBank 5.0: a major update to the DrugBank database for 2018

Drug-target interaction prediction with Bipartite Local Models and hubness-aware regression

Quantitative and Systems Pharmacology. 1. In Silico Prediction of Drug-Target Interactions of Natural Products Enables New Targeted Cancer Therapy

DGIdb 3.0: a redesign and expansion of the drug–gene interaction database

VB-MK-LMF: fusion of drugs, targets and interactions using variational Bayesian multiple kernel logistic matrix factorization

Drug-target interaction prediction using ensemble learning and dimensionality reduction.

A Computational-Based Method for Predicting Drug–Target Interactions by Using Stacked Autoencoder Deep Neural Network

In silico prediction of drug-target interaction networks based on drug chemical structure and protein sequences

Drug-target interaction prediction by integrating multiview network data

Deep mining heterogeneous networks of biomedical linked data to predict novel drug‐target associations

An Ameliorated Prediction of Drug–Target Interactions Based on Multi-Scale Discrete Wavelet Transform and Network Features

Deep Matrix Factorization Models for Recommender Systems

iDTI-ESBoost: Identification of Drug Target Interaction Using Evolutionary and Structural Features with Boosting

Prediction of Drug–Target Interaction Networks from the Integration of Protein Sequences and Drug Chemical Structures

Predicting the Reliability of Drug-target Interaction Predictions with Maximum Coverage of Target Space

Link Mining for Kernel-based Compound-Protein Interaction Predictions Using a Chemogenomics Approach

Drug-Target Interaction Prediction with Graph Regularized Matrix Factorization

SimBoost: a read-across approach for predicting drug–target binding affinities using gradient boosting machines

A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information

A kernel matrix dimension reduction method for predicting drug-target interaction

Deep-Learning-Based Drug-Target Interaction Prediction.

DrugRPE: Random projection ensemble approach to drug-target interaction prediction

Improved prediction of protein-protein interactions using novel negative samples, features, and an ensemble classifier

Predicting Drug–Target Interactions With Multi-Information Fusion

Predicting drug-target interactions by dual-network integrated logistic matrix factorization

Improved genome-scale multi-target virtual screening via a novel collaborative filtering approach to cold-start problem

A comprehensive map of molecular drug targets

Drug-target interaction prediction via class imbalance-aware ensemble learning

KEGG: new perspectives on genomes, pathways, diseases and drugs

Pharos: Collating protein information to shed light on the druggable genome

The ChEMBL database in 2017

RFDT: A Rotation Forest-based Predictor for Predicting Drug-Target Interactions Using Drug Structure and Protein Sequence Information.

DrugCentral: online drug compendium