Drug Target Interaction Prediction Using Machine Learning Techniques – A Review

Drug discovery is a key process, given the rising and ubiquitous demand for medication to stay in good shape right through the course of one’s life. Drugs are small molecules that inhibit or activate the function of a protein, offering patients a host of therapeutic benefits. Drug design is the inventive process of finding new medication, based on targets or proteins. Identifying new drugs is a process that involves time and money. This is where computer-aided drug design helps cut time and costs. Drug design needs drug targets that are a protein and a drug compound, with which the interaction between a drug and a target is established. Interaction, in this context, refers to the process of discovering protein binding sites, which are protein pockets that bind with drugs. Pockets are regions on a protein macromolecule that bind to drug molecules. Researchers have been at work trying to determine new Drug Target Interactions (DTI) that predict whether or not a given drug molecule will bind to a target. Machine learning (ML) techniques help establish the interaction between drugs and their targets, using computer-aided drug design. This paper aims to explore ML techniques better for DTI prediction and boost future research. Qualitative and quantitative analyses of ML techniques show that several have been applied to predict DTIs, employing a range of classifiers. Though DTI prediction improves with negative drug target pairs (DTP), the lack of true negative DTPs has led to the use a particular dataset of drugs and targets. Using dynamic DTPs improves DTI prediction. Little attention has so far been paid to developing a new classifier for DTI classification, and there is, unquestionably, a need for better ones

Positive-unlabelled learning for identifying new candidate dietary restriction-related genes among ageing-related genes

Dietary Restriction (DR) is one of the most popular anti-ageing interventions; recently, Machine Learning (ML) has been explored to identify potential DR-related genes among ageing-related genes, aiming to minimize costly wet lab experiments needed to expand our knowledge on DR. However, to train a model from positive (DR-related) and negative (non-DR-related) examples, the existing ML approach naively labels genes without known DR relation as negative examples, assuming that lack of DR-related annotation for a gene represents evidence of absence of DR-relatedness, rather than absence of evidence. This hinders the reliability of the negative examples (non-DR-related genes) and the method’s ability to identify novel DR-related genes. This work introduces a novel gene prioritization method based on the two-step Positive-Unlabelled (PU) Learning paradigm: using a similarity-based, KNN-inspired approach, our method first selects reliable negative examples among the genes without known DR associations. Then, these reliable negatives and all known positives are used to train a classifier that effectively differentiates DR-related and non-DR-related genes, which is finally employed to generate a more reliable ranking of promising genes for novel DR-relatedness. Our method significantly outperforms (p<0.05) the existing state-of-the-art approach in three predictive accuracy metrics with up to ∼40% lower computational cost in the best case, and we identify 4 new promising DR-related genes (PRKAB1, PRKAB2, IRS2, PRKAG1), all with evidence from the existing literature supporting their potential DR-related role

Predicting potential drugs and drug-drug interactions for drug repositioning

The purpose of drug repositioning is to predict novel treatments for existing drugs. It saves time and reduces cost in drug discovery, especially in preclinical procedures. In drug repositioning, the challenging objective is to identify reasonable drugs with strong evidence. Recently, benefiting from various types of data and computational strategies, many methods have been proposed to predict potential drugs. Signature-based methods use signatures to describe a specific disease condition and match it with drug-induced transcriptomic profiles. For a disease signature, a list of potential drugs is produced based on matching scores. In many studies, the top drugs on the list are identified as potential drugs and verified in various ways. However, there are a few limitations in existing methods: (1) For many diseases, especially cancers, the tissue samples are often heterogeneous and multiple subtypes are involved. It is challenging to identify a signature from such a group of profiles. (2) Genes are treated as independent elements in many methods, while they may associate with each other in the given condition. (3) The disease signatures cannot identify potential drugs for personalized treatments. In order to address those limitations, I propose three strategies in this dissertation. (1) I employ clustering methods to identify sub-signatures from the heterogeneous dataset, then use a weighting strategy to concatenate them together. (2) I utilize human protein complex (HPC) information to reflect the dependencies among genes and identify an HPC signature to describe a specific type of cancer. (3) I use an HPC strategy to identify signatures for drugs, then predict a list of potential drugs for each patient. Besides predicting potential drugs directly, more indications are essential to enhance my understanding in drug repositioning studies. The interactions between biological and biomedical entities, such as drug-drug interactions (DDIs) and drug-target interactions (DTIs), help study mechanisms behind the repurposed drugs. Machine learning (ML), especially deep learning (DL), are frontier methods in predicting those interactions. Network strategies, such as constructing a network from interactions and studying topological properties, are commonly used to combine with other methods to make predictions. However, the interactions may have different functions, and merging them in a single network may cause some biases. In order to solve it, I construct two networks for two types of DDIs and employ a graph convolutional network (GCN) model to concatenate them together. In this dissertation, the first chapter introduces background information, objectives of studies, and structure of the dissertation. After that, a comprehensive review is provided in Chapter 2. Biological databases, methods and applications in drug repositioning studies, and evaluation metrics are discussed. I summarize three application scenarios in Chapter 2. The first method proposed in Chapter 3 considers the issue of identifying a cancer gene signature and predicting potential drugs. The k-means clustering method is used to identify highly reliable gene signatures. The identified signature is used to match drug profiles and identify potential drugs for the given disease. The second method proposed in Chapter 4 uses human protein complex (HPC) information to identify a protein complex signature, instead of a gene signature. This strategy improves the prediction accuracy in the experiments of cancers. Chapter 5 introduces the signature-based method in personalized cancer medicine. The profiles of a given drug are used to identify a drug signature, under the HPC strategy. Each patient has a profile, which is matched with the drug signature. Each patient has a different list of potential drugs. Chapter 6 propose a graph convolutional network with multi-kernel to predict DDIs. This method constructs two DDI kernels and concatenates them in the GCN model. It achieves higher performance in predicting DDIs than three state-of-the-art methods. In summary, this dissertation has proposed several computational algorithms for drug repositioning. Experimental results have shown that the proposed methods can achieve very good performance

Automated Machine Learning for Positive-Unlabelled Learning

Positive-Unlabelled (PU) learning is a field of machine learning that involves learning classifiers from data consisting of positive class and unlabelled instances. That is, instances that may be either positive or negative, but the label is unknown. PU learning differs from standard binary classification due to the absence of negative instances. This difference is non-trivial and requires differing classification frameworks and evaluation metrics. This thesis looks to address gaps in the PU learning literature and make PU learning more accessible to non-experts by introducing Automated Machine Learning (Auto-ML) systems specific to PU learning. Three such systems have been developed, GA-Auto-PU, a Genetic Algorithm (GA)-based Auto-ML system, BO-Auto-PU, a Bayesian Optimisation (BO)-based Auto-ML system, and EBO-Auto-PU, an Evolutionary/Bayesian Optimisation (EBO) hybrid-based Auto-ML system. These three Auto-ML systems are three primary contributions of this work. EBO, the optimiser component of EBO-Auto-PU, is by itself a novel optimisation method developed in this work that has proved effective for the task of Auto-ML and represents another contribution. EBO was developed with the aim of acting as a trade-off between GA, which achieved high predictive performance but at high computational expense, and BO, which, when utilised by the Auto-PU system, did not perform as well as the GA-based system but did execute much faster. EBO achieved this aim, providing high predictive performance with a computational runtime much faster than the GA-based system, and not substantially slower than the BO-based system. The proposed Auto-ML systems for PU learning were evaluated on three versions of 40 datasets, thus evaluated on 120 learning tasks in total. The 40 datasets consist of 20 real-world biomedical datasets and 20 synthetic datasets. The main evaluation measure was the F-measure, a popular measure in PU learning. Based on the F-measure results, the three proposed systems outperformed in general two baseline PU learning methods, usually with statistically significant results. Among the three proposed systems, there was no statistically significance difference between their results in general, whilst a version of the EBO-Auto-PU system performed overall slightly better than the other systems, in terms of F-measure. The two other main contributions of this work relate specifically to the field of PU learning. Firstly, in this work we present and utilise a robust evaluation approach. Evaluating PU learning classifiers is non-trivial and little guidance has been provided in the literature on how to do so. In this work, we present a clear framework for evaluation and use this framework to evaluate the proposed systems. Secondly, when evaluating the proposed systems, an analysis of the most frequently selected components of the optimised PU learning algorithm is presented. That is, the components that constitute the PU learning algorithms produced by the optimisers (for example, the choice of classifiers used in the algorithm, the number of iterations, etc.). This analysis is used to provide guidance on the construction of PU learning algorithms for specific dataset characteristics