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

Multi-task and Multi-view Learning for Predicting Adverse Drug Reactions

Adverse drug reactions (ADRs) present a major concern for drug safety and are a major obstacle in modern drug development. They account for about one-third of all late-stage drug failures, and approximately 4% of all new chemical entities are withdrawn from the market due to severe ADRs. Although off-target drug interactions are considered to be the major causes of ADRs, the adverse reaction profile of a drug depends on a wide range of factors such as specific features of drug chemical structures, its ADME/PK properties, interactions with proteins, the metabolic machinery of the cellular environment, and the presence of other diseases and drugs. Hence computational modeling for ADRs prediction is highly complex and challenging. We propose a set of statistical learning models for effective ADRs prediction systematically from multiple perspectives. We first discuss available data sources for protein-chemical interactions and adverse drug reactions, and how the data can be represented for effective modeling. We also employ biological network analysis approaches for deeper understanding of the chemical biological mechanisms underlying various ADRs. In addition, since protein-chemical interactions are an important component for ADRs prediction, identifying these interactions is a crucial step in both modern drug discovery and ADRs prediction. The performance of common supervised learning methods for predicting protein-chemical interactions have been largely limited by insufficient availability of binding data for many proteins. We propose two multi-task learning (MTL) algorithms for jointly predicting active compounds of multiple proteins, and our methods outperform existing states of the art significantly. All these related data, methods, and preliminary results are helpful for understanding the underlying mechanisms of ADRs and further studies. ADRs data are complex and noisy, and in many cases we do not fully understand the molecular mechanisms of ADRs. Due to the noisy and heterogeneous data set available for some ADRs, we propose a sparse multi-view learning (MVL) algorithm for predicting a specific ADR - drug-induced QT prolongation, a major life-threatening adverse drug effect. It is crucial to predict the QT prolongation effect as early as possible in drug development. MVL algorithms work very well when complex data from diverse domains are involved and only limited labeled examples are available. Unlike existing MVL methods that use L2-norm co-regularization to obtain a smooth objective function, we propose an L1-norm co-regularized MVL algorithm for predicting QT prolongation, reformulate the objective function, and obtain its gradient in the analytic form. We optimize the decision functions on all views simultaneously and achieve 3-4 fold higher computational speedup, comparing to previous L2-norm co-regularized MVL methods that alternately optimizes one view with the other views fixed until convergence. L1-norm co-regularization enforces sparsity in the learned mapping functions and hence the results are expected to be more interpretable. The proposed MVL method can only predict one ADR at a time. It would be advantageous to predict multiple ADRs jointly, especially when these ADRs are highly related. Advanced modeling techniques should be investigated to better utilize ADR data for more effective ADRs prediction. We study the quantitative relationship among drug structures, drug-protein interaction profiles, and drug ADRs. We formalize the modeling problem as a multi-view (drug structure data and drug-protein interaction profile data) multi-task (one drug may cause multiple ADRs and each ADR is a task) classification problem. We apply the co-regularized MVL on each ADR and use regularized MTL to increase the total sample size and improve model performance. Experimental studies on the ADR data set demonstrate the effectiveness of our MVMT algorithm. Cluster analysis and significant feature identification using the results of our models reveal interesting hidden insight. In summary, we use computational methods such as biological network analysis, multi-task learning, multi-view learning, and inductive multi-view multi-task learning to systematically investigate the modeling of various ADRs, and construct highly accurate models for ADRs prediction. We also have significant contribution on proposing novel supervised and semi-supervised learning algorithms, which can be applied to many other real-world applications

Assessment of modeling strategies for drug response prediction in cell lines and xenografts

Despite significant progress in cancer research, effective cancer treatment is still a challenge. Cancer treatment approaches are shifting from standard cytotoxic chemotherapy regimens towards a precision oncology paradigm, where a choice of treatment is personalized, i.e. based on a tumor’s molecular features. In order to match tumor molecular features with therapeutics we need to identify biomarkers of response and build predictive models. Recent growth of large-scale pharmacogenomics resources which combine drug sensitivity and multi-omics information on a large number of samples provides necessary data for biomarker identification and drug response modelling. However, although many efforts of using this information for drug response prediction have been made, our ability to accurately predict drug response using genetic data remains limited. In this work we used pharmacogenomics data from the largest publicly available studies in order to systematically assess various aspects of the drug response model-building process with the ultimate goal of improving prediction accuracy. We applied several machine learning methods (regularized regression, support vector machines, random forest) for predicting response to a number of drugs. We found that while accuracy of response prediction varies across drugs (in most of the cases R2 values vary between 0.1 and 0.3), different machine learning algorithms applied for the the same drug have similar prediction performance. Experiments with a range of different training sets for the same drug showed that predictive power of a model depends on the type of molecular data, the selected drug response metric, and the size of the training set. It depends less on number of features selected for modelling and on class imbalance in training set. We also implemented and tested two methods for improving consistency for pharmacogenomics data coming from different datasets. We tested our ability to correctly predict response in xenografts and patients using models trained on cell lines. Only in a fraction of the tested cases we managed to get reasonably accurate predictions, particularly in case of response to erlotinib in the NSCLC xenograft cohort, and in cases of responses to erlotinib and docetaxel in the NSCLC and BRCA patient cohorts respectively. This work also includes two applied pharmacogenomics analyses. The first is an analysis of a drug-sensitivity screen performed on a panel of Burkitt cell lines. This combines unsupervised data exploration with supervised modelling. The second is an analysis of drug-sensitivity data for the DKFZ-608 compound and the generation of the corresponding response prediction model. In summary, we applied machine learning techniques to available high-throughput pharmacogenomics data to study the determinants of accurate drug response prediction. Our results can help to draft guidelines for building accurate models for personalized drug response prediction and therefore contribute to advancing of precision oncology

Artificial Intelligence in Oncology Drug Discovery and Development

There exists a profound conflict at the heart of oncology drug development. The efficiency of the drug development process is falling, leading to higher costs per approved drug, at the same time personalised medicine is limiting the target market of each new medicine. Even as the global economic burden of cancer increases, the current paradigm in drug development is unsustainable. In this book, we discuss the development of techniques in machine learning for improving the efficiency of oncology drug development and delivering cost-effective precision treatment. We consider how to structure data for drug repurposing and target identification, how to improve clinical trials and how patients may view artificial intelligence

Integrative bioinformatics and graph-based methods for predicting adverse effects of developmental drugs

Adverse drug effects are complex phenomena that involve the interplay between drug molecules and their protein targets at various levels of biological organisation, from molecular to organismal. Many factors are known to contribute toward the safety profile of a drug, including the chemical properties of the drug molecule itself, the biological properties of drug targets and other proteins that are involved in pharmacodynamics and pharmacokinetics aspects of drug action, and the characteristics of the intended patient population. A multitude of scattered publicly available resources exist that cover these important aspects of drug activity. These include manually curated biological databases, high-throughput experimental results from gene expression and human genetics resources as well as drug labels and registered clinical trial records. This thesis proposes an integrated analysis of these disparate sources of information to help bridge the gap between the molecular and the clinical aspects of drug action. For example, to address the commonly held assumption that narrowly expressed proteins make safer drug targets, an integrative data-driven analysis was conducted to systematically investigate the relationship between the tissue expression profile of drug targets and the organs affected by clinically observed adverse drug reactions. Similarly, human genetics data were used extensively throughout the thesis to compare adverse symptoms induced by drug molecules with the phenotypes associated with the genes encoding their target proteins. One of the main outcomes of this thesis was the generation of a large knowledge graph, which incorporates diverse molecular and phenotypic data in a structured network format. To leverage the integrated information, two graph-based machine learning methods were developed to predict a wide range of adverse drug effects caused by approved and developmental therapies

Modeling and prediction of advanced prostate cancer

Background: Prostate cancer (PCa) is the most commonly diagnosed cancer and second leading cause of cancer-related deaths for men in Western countries. The advanced form of the disease is life-threatening with few options for curative therapies. The development of novel therapeutic alternatives would greatly benefit from a more comprehensive and tailored mathematical and statistical methodology. In particular, statistical inference of treatment effects and the prediction of time-dependent effects in both preclinical and clinical studies remains a challenging yet interesting opportunity for applied mathematicians. Such methods are likely to improve the reproducibility and translatability of results and offer possibility for novel holistic insights into disease progression, diagnosis, and prognosis. Methods: Several novel statistical and mathematical techniques were developed over the course of this thesis work for the in vivo modeling of PCa treatment responses. A matching-based, blinded randomized allocation procedure for preclinical experiments was developed that provides assistance for the statistical design of animal intervention studies, e.g., through power analysis and accounting for the stratification of individuals. For the post-intervention testing of treatment effects, two novel mixed-effects models were developed that aim to address the characteristic challenges of preclinical longitudinal experiments, including the heterogeneous response profiles observed in animal studies. Subsequently, a Finnish clinical PCa hospital registry cohort was inspected with a strong emphasis on prostate-specific antigen (PSA), the most commonly used PCa marker. After exploring the PSA trends using penalized splines, a generalized mixed-effects prediction model was implemented with a focus on the ultra-sensitive range of the PSA assay. Finally, for metastatic, aggressive PCa, an ensemble Cox regression methodology was developed for overall survival prediction in the DREAM 9.5 mCRPC Challenge based on open datasets from controlled clinical trials. Results: The advantages of the improved experimental design and two proposed statistical models were demonstrated in terms of both increased statistical power and accuracy in simulated and real preclinical testing settings. Penalized regression models applied to the clinical patient datasets support the use of PSA in the ultra-sensitive range together with a model for relapse prediction. Furthermore, the novel ensemble-based Cox regression model that was developed for the overall survival prediction in advanced PCa outperformed the state-of-the-art benchmark and all other models submitted to the Challenge and provided novel predictors of disease progression and treatment responses. Conclusions: The methods and results provide preclinical researchers and clinicians with novel tools for comprehensive modeling and prediction of PCa. All methodology is available as open source R statistical software packages and/or web-based graphical user interfaces