• Biocatalysis
• Molecular Dynamics Simulation
• Enzymes/chemistry
• Enzymes/metabolism
• Machine Learning
• Deep Learning
• Algorithms
• Ligands

• Invest Northern Ireland
• Research Councils UK

• Antiviral Agents/pharmacology
• Deep Learning
• Monkeypox virus/drug effects
• Drug Repositioning/methods
• Pyrazines/pharmacology
• Molecular Docking Simulation
• Dibenzothiepins
• Amides/pharmacology
• Ribavirin/pharmacology
• Triazines/pharmacology
• Mpox (monkeypox)/drug therapy
• Mpox (monkeypox)/virology
• Humans
• Machine Learning
• Morpholines
• Pyridones

• Antiviral Agents/therapeutic use
• Antiviral Agents/pharmacology
• Machine Learning
• Drug Synergism
• Drug Therapy, Combination
• Humans
• Ribavirin/therapeutic use
• Herpesvirus 1, Human/drug effects
• Herpesvirus 1, Human/physiology
• Acyclovir/therapeutic use
• Acyclovir/administration & dosage
• Acyclovir/pharmacology
• Virus Diseases/drug therapy

• Deep learning
• Drug repurposing
• Graph attention network (GAT)
• Machine learning
• Practical survey
• Tensor-matrix-tensor formulation

• Drug Repositioning/methods
• Neural Networks, Computer
• Machine Learning
• Deep Learning
• Drug Discovery/methods
• Algorithms
• Computational Biology/methods
• Humans

• DTI prediction
• graph neural network
• hierarchical multimodal self-attention
• multimodal fusion

• Neural Networks, Computer
• Deep Learning
• Proteins/chemistry
• Proteins/metabolism
• Humans
• Algorithms
• Computational Biology/methods

• 2021YFC2100101 National Key R&D Program of China
• 62225109 National Natural Science Foundation of China
• National Key R&D Program of China

• Humans
• Drug Repositioning
• Computational Biology

• T15 LM012500 NLM NIH HHS

• artificial intelligence
• chemogenomics
• compound-protein interaction prediction
• drug discovery
• scoring function

• COVID-19
• Deep learning
• Drug repurposing
• Matrix factorization

• Class imbalance
• Drug discovery
• Drug–target interactions
• Extreme learning machine
• Spherical search

• artificial intelligence
• computational toxicology
• decision quality
• drug discovery
• lead-to-candidate
• machine learning

Inflammatory bowel diseases (IBDs) comprising ulcerative colitis, Crohn’s disease and microscopic colitis are characterized by chronic inflammation of the gastrointestinal tract. IBD has spread around the world and is becoming more prevalent at an alarming rate in developing countries whose societies have become more westernized. Cell therapy, intestinal microecology, apheresis therapy, exosome therapy and small molecules are emerging therapeutic options for IBD. Currently, it is thought that low-molecular-mass substances with good oral bio-availability and the ability to permeate the cell membrane to regulate the action of elements of the inflammatory signaling pathway are effective therapeutic options for the treatment of IBD. Several small molecule inhibitors are being developed as a promising alternative for IBD therapy. The use of highly efficient and time-saving techniques, such as computational methods, is still a viable option for the development of these small molecule drugs. The computer-aided (in silico) discovery approach is one drug development technique that has mostly proven efficacy. Computational approaches when combined with traditional drug development methodology dramatically boost the likelihood of drug discovery in a sustainable and cost-effective manner. This review focuses on the modern drug discovery approaches for the design of novel IBD drugs with an emphasis on the role of computational methods. Some computational approaches to IBD genomic studies, target identification, and virtual screening for the discovery of new drugs and in the repurposing of existing drugs are discussed.

• Inflammatory bowel disease
• Computer-aided drug design
• Janus Kinase
• Molecular docking
• Genome-wide association study
• Molecular dynamics simulation

• Humans
• Inflammatory Bowel Diseases/drug therapy
• Inflammatory Bowel Diseases/metabolism
• Intestines
• Drug Discovery

• Artificial intelligence
• Deep learning
• Digital twining
• Drug discovery
• Drug dosing optimization
• Drug sensitivity and response
• Drug side-effects
• Drug–drug similarity
• Drug–target interactions
• Explainable artificial intelligence

• Cairo University

• attention mechanism
• convolutional neural network (CNN)
• deep learning - artificial neural network
• drug-protein interactions (DPIs)
• variational autoencoder (VAE)

• Deep Learning
• Drug Development
• Drug Discovery/methods
• Neural Networks, Computer
• Proteins/chemistry

Recently, drug repositioning has received considerable attention for its advantage to pharmaceutical industries in drug development. Artificial intelligence techniques have greatly enhanced drug reproduction by discovering therapeutic drug profiles, side effects, and new target proteins. However, as the number of drugs increases, their targets and enormous interactions produce imbalanced data that might not be preferable as an input to a prediction model immediately.

This paper proposes a novel scheme for predicting drug–target interactions (DTIs) based on drug chemical structures and protein sequences. The drug Morgan fingerprint, drug constitutional descriptors, protein amino acid composition, and protein dipeptide composition were employed to extract the drugs and protein’s characteristics. Then, the proposed approach for extracting negative samples using a support vector machine one-class classifier was developed to tackle the imbalanced data problem feature sets from the drug–target dataset. Negative and positive samplings were constructed and fed into different prediction algorithms to identify DTIs. A 10-fold CV validation test procedure was applied to assess the predictability of the proposed method, in addition to the study of the effectiveness of the chemical and physical features in the evaluation and discovery of the drug–target interactions.

Our experimental model outperformed existing techniques concerning the curve for receiver operating characteristic (AUC), accuracy, precision, recall F-score, mean square error, and MCC. The results obtained by the AdaBoost classifier enhanced prediction accuracy by 2.74%, precision by 1.98%, AUC by 1.14%, F-score by 3.53%, and MCC by 4.54% over existing methods.

• Data balancing
• Drug–target interaction
• Machine learning
• Support vector machine

• Drug-target interaction
• Feature selection
• Negative samples
• Random selection

• attentions at neighbor-type level and at network level
• drug–protein interaction
• graph neural network with neighbor node-level attention
• multi-type neighbors enhanced global topology
• multiple drug–protein heterogenous networks
• three-dimensional convolutional neural network

• Machine learning
• algorithms
• deep learning
• drug metabolism
• metabolites
• molecular docking

• Databases
• Diabetes mellitus
• Environmental factors
• Natural products
• Network pharmacology

• Diabetes Mellitus/drug therapy
• Humans
• Phytotherapy
• Plants, Medicinal

• Algorithms
• Drug Development/methods
• Drug Repositioning
• Humans
• Neural Networks, Computer
• Proteins

• artificial intelligence-based drug discovery
• benchmark tool
• de novo drug design
• deep learning
• drug–target interaction
• molecular representation
• virtual screening

• Animals
• Deep Learning
• Drug Design
• Drug Development
• Drug Discovery
• Humans
• Neural Networks, Computer
• Pharmaceutical Preparations/chemistry
• Surveys and Questionnaires

• 2017M3C9A5028690 National Research Foundation of Korea

• Binary interaction
• Binding affinity prediction
• Drug–target interaction
• Gradient boosting machine
• Semi-supervised learning
• Transfer learning

• Drug-protein interactions
• databases
• drug repurposing
• drug side-effects
• drug structure
• drug-protein interaction prediction
• protein sequence

• artificial intelligence
• data-driven
• drug discovery
• machine learning

• Drug-target interaction
• community detection
• label correlation
• multi-label learning

• The National Key Research and Development Program of China

Computational methods for predicting the macromolecular targets of drugs and drug-like compounds have evolved as a key technology in drug discovery. However, the established validation protocols leave several key questions regarding the performance and scope of methods unaddressed. For example, prediction success rates are commonly reported as averages over all compounds of a test set and do not consider the structural relationship between the individual test compounds and the training instances. In order to obtain a better understanding of the value of ligand-based methods for target prediction, we benchmarked a similarity-based method and a random forest based machine learning approach (both employing 2D molecular fingerprints) under three testing scenarios: a standard testing scenario with external data, a standard time-split scenario, and a scenario that is designed to most closely resemble real-world conditions. In addition, we deconvoluted the results based on the distances of the individual test molecules from the training data. We found that, surprisingly, the similarity-based approach generally outperformed the machine learning approach in all testing scenarios, even in cases where queries were structurally clearly distinct from the instances in the training (or reference) data, and despite a much higher coverage of the known target space.

• drug discovery
• machine learning
• molecular fingerprints
• molecular similarity
• random forest
• target prediction

• Drug Discovery
• Knowledge Bases
• Machine Learning
• Molecular Targeted Therapy
• Reproducibility of Results

Deep learning is an effective method to capture drug-target binding affinity, but low accuracy is still an obstacle to be overcome. Thus, we propose a novel predictor for drug-target binding affinity based on dipeptide frequency of word frequency encoding and a hybrid graph convolutional network. Word frequency characteristics of natural language are used to improve the frequency characteristics of peptides to express target proteins. For each drug molecules, the five different features of drug atoms and the atomic bond relationships are expressed as graphs. The obtained protein features and graph structure are used as the input of convolution neural network and the input of graph convolution neural network, respectively. A prediction model is established to predict the drug affinity by calculating the hidden relationship. In the KIBA data set test experiment, the consistency coefficient of the model is 0.901, which is 0.01 higher than the existing model, and the MSE (mean square error) of the model is 0.126, which is 5% lower than the existing model. In Davis data set test experiment, the consistency coefficient of the model is 0.895, which is 0.006 higher than the existing model, and the MSE of the model is 0.220, which is 4% lower than the existing model. These results show that our proposed method can not only predict the affinity better than those existing models, but also outperform unitary deep learning approaches.

• deep learning
• dipeptide frequency of word frequency
• drug-target binding affinity
• graph convolutional network
• variable importance measures

• Bioinformatics
• Class imbalance
• Classification
• Computer Science
• Drug-Target
• Ensemble classifier
• Feature engineering

• convolutional neural networks
• deep learning
• drug-target affinity prediction
• generative adversarial networks
• semi-supervised

• cascade deep forest
• drug-target interaction
• ensemble learning
• machine learning

• Machine learning
• drug discovery
• drug repurposing
• drug-target interaction
• molecular fingerprint
• similarity measure.

• Data imbalance
• Drug-target interaction
• Feature extraction
• Feature selection
• Molecular substructure fingerprint
• XGBoost classifier

Drug repurposing plays an important role in screening old drugs for new therapeutic efficacy. The existing methods commonly treat prediction of drug-target interaction as a problem of binary classification, in which a large number of randomly sampled drug-target pairs accounting for over 50% of the entire training dataset are necessarily required. Such a large number of negative examples that do not come from experimental observations inevitably decrease the credibility of predictions. In this study, we propose a multi-label learning framework to find new uses for old drugs and discover new drugs for known target genes. In the framework, each drug is treated as a class label and its target genes are treated as the class-specific training data to train a supervised learning model of l₂-regularized logistic regression. As such, the inter-drug associations are explicitly modelled into the framework and all the class-specific training data come from experimental observations. In addition, the data constraint is less demanding, for instance, the chemical substructures of a drug are no longer needed and the novel target genes are inferred only from the underlying patterns of the known genes targeted by the drug. Stratified multi-label cross-validation shows that 84.9% of known target genes have at least one drug correctly recognized, and the proposed framework correctly recognizes 86.73% of the independent test drug-target interactions (DTIs) from DrugBank. These results show that the proposed framework could generalize well in the large drug/class space without the information of drug chemical structures and target protein structures. Furthermore, we use the trained model to predict new drugs for the known target genes, identify new genes for the old drugs, and infer new associations between old drugs and new disease phenotypes via the OMIM database. Gene ontology (GO) enrichment analyses and the disease associations reported in recent literature provide supporting evidences to the computational results, which potentially shed light on new clinical therapies for new and/or old disease phenotypes.

• drug repurposing
• drug-disease associations
• drug-target interaction
• multi-label learning
• stratified multi-label cross validation

• Computational models
• Drug efficacy prediction
• Multi linear regression
• Oral squamous cell carcinoma
• Precision medicine

• Adult
• Aged
• Algorithms
• Antineoplastic Agents/pharmacology
• Apoptosis
• Carcinoma, Squamous Cell/drug therapy
• Computer Simulation
• Female
• Humans
• Least-Squares Analysis
• Linear Models
• Male
• Medical Oncology
• Middle Aged
• Mouth Neoplasms/drug therapy
• Multivariate Analysis
• Precision Medicine
• Reproducibility of Results

• 3D scaffolds
• CRISPR/Cas9 systems
• Cardiac tissue engineering
• Machine learning

• R41 OD024874 NIH HHS

• machine learning
• data mining

• Algorithms
• Computational Biology/methods
• Data Mining
• Datasets as Topic
• Drug Development/methods
• Humans
• Machine Learning
• Proteins/chemistry
• Proteins/metabolism
• ROC Curve
• Software

• Chemical similarity search
• Drug-target profile
• Panel docking
• Target identification

Prediction of new drug-target interactions is critically important as it can lead the researchers to find new uses for old drugs and to disclose their therapeutic profiles or side effects. However, experimental prediction of drug-target interactions is expensive and time-consuming. As a result, computational methods for predictioning new drug-target interactions have gained a tremendous interest in recent times. Here we present iDTI-ESBoost, a prediction model for identification of drug-target interactions using evolutionary and structural features. Our proposed method uses a novel data balancing and boosting technique to predict drug-target interaction. On four benchmark datasets taken from a gold standard data, iDTI-ESBoost outperforms the state-of-the-art methods in terms of area under receiver operating characteristic (auROC) curve. iDTI-ESBoost also outperforms the latest and the best-performing method found in the literature in terms of area under precision recall (auPR) curve. This is significant as auPR curves are argued as suitable metric for comparison for imbalanced datasets similar to the one studied here. Our reported results show the effectiveness of the classifier, balancing methods and the novel features incorporated in iDTI-ESBoost. iDTI-ESBoost is a novel prediction method that has for the first time exploited the structural features along with the evolutionary features to predict drug-protein interactions. We believe the excellent performance of iDTI-ESBoost both in terms of auROC and auPR would motivate the researchers and practitioners to use it to predict drug-target interactions. To facilitate that, iDTI-ESBoost is implemented and made publicly available at: http://farshidrayhan.pythonanywhere.com/iDTI-ESBoost/.

• Drug-target interaction prediction
• Matrix factorization
• Multiple kernel learning
• Probabilistic graphical models
• Variational Bayes

• Algorithms
• Area Under Curve
• Bayes Theorem
• Drug Interactions
• Humans

• ÚNKP-16-3-III. New National Excellence Program of the Ministry of Human Capacities
• OTKA
• János Bolyai Research Scholarship of the Hungarian Academy of Sciences