Current Topics in Medicinal Chemistry - Volume 18, Issue 12, 2018

Multi-target and combinatorial therapies have been focused for the past several decades. These approaches achieved considerable therapeutic efficacy by modulating the activities of the targets in complex diseases such as HIV-1 infection, cancer and diabetes disease. Most of the diseases cannot be treated efficiently in terms of single gene target, because it involves the cessation of the coordinated function of distinct gene groups. Most of the cellular components work efficiently by interacting with other cellular components and all these interactions together represent interactome. This interconnectivity shows that a defect in a single gene may not be restricted to the gene product itself, but may spread along the network. So, drug development must be based on the network-based perspective of disease mechanisms. Many systematic diseases like neurodegenerative disorders, cancer and cardiovascular cannot be treated efficiently by the single gene target strategy because these diseases involve the complex biological machinery. In clinical trials, many mono-therapies have been found to be less effective. In mono-therapies, the long term treatment, for the systematic diseases make the diseases able to acquired resistance because of the disease nature of the natural evolution of feedback loop and pathway redundancy. Multi-target drugs might be more efficient. Multi-target therapeutics might be less vulnerable because of the inability of the biological system to resist multiple actions. In this study, we will overview the recent advances in the development of methodologies for the identification of drug target interaction and its application in the poly-pharmacology profile of the drug.

Synergistic drug combinations play an important role in the treatment of complex diseases. The identification of effective drug combination is vital to further reduce the side effects and improve therapeutic efficiency. In previous years, in vitro method has been the main route to discover synergistic drug combinations. However, many limitations of time and resource consumption lie within the in vitro method. Therefore, with the rapid development of computational models and the explosive growth of large and phenotypic data, computational methods for discovering synergistic drug combinations are an efficient and promising tool and contribute to precision medicine. It is the key of computational methods how to construct the computational model. Different computational strategies generate different performance. In this review, the recent advancements in computational methods for predicting effective drug combination are concluded from multiple aspects. First, various datasets utilized to discover synergistic drug combinations are summarized. Second, we discussed feature-based approaches and partitioned these methods into two classes including feature-based methods in terms of similarity measure, and feature-based methods in terms of machine learning. Third, we discussed network-based approaches for uncovering synergistic drug combinations. Finally, we analyzed and prospected computational methods for predicting effective drug combinations.

Pulmonary arterial hypertension (PAH) is a devastating disease without effective drugs available for its treatment. An in-depth exploration of the pathogenesis of PAH, as well as inquiry into potential therapeutic targets, remains an urgent issue. Non-coding RNAs (ncRNAs) have arisen as key players in malignant tumors, cardiovascular diseases and more recently in PAH progression and development. Network pharmacology is a new discipline based on system biology, which can predict potential therapeutic targets in diseases regulated by multiple genes. In this review, we discuss the current knowledge of ncRNAs and network pharmacology regulated genes involved in PAH, as well as the search for potential drug targets for PAH.

Toxicity evaluation is an important part of the preclinical safety assessment of new drugs, which is directly related to human health and the fate of drugs. It is of importance to study how to evaluate drug toxicity accurately and economically. The traditional in vitro and in vivo toxicity tests are laborious, time-consuming, highly expensive, and even involve animal welfare issues. Computational methods developed for drug toxicity prediction can compensate for the shortcomings of traditional methods and have been considered useful in the early stages of drug development. Numerous drug toxicity prediction models have been developed using a variety of computational methods. With the advance of the theory of machine learning and molecular representation, more and more drug toxicity prediction models are developed using a variety of machine learning methods, such as support vector machine, random forest, naive Bayesian, back propagation neural network. And significant advances have been made in many toxicity endpoints, such as carcinogenicity, mutagenicity, and hepatotoxicity. In this review, we aimed to provide a comprehensive overview of the machine learning based drug toxicity prediction studies conducted in recent years. In addition, we compared the performance of the models proposed in these studies in terms of accuracy, sensitivity, and specificity, providing a view of the current state-of-the-art in this field and highlighting the issues in the current studies.

Over the past ten years, the number of three-dimensional protein structures identified by advanced science and technology increases, and the gene information becomes more available than ever before as well. The development of computing science becomes another driving force which makes it possible to use computational methods effectively in various phases of the drug design and research. Now Structure-Based Drug Design (SBDD) tools are widely used to help researchers to predict the position of small molecules within a three-dimensional representation of the protein structure and estimate the affinity of ligands to target protein with considerable accuracy and efficiency. They also accelerate discovery speed of potent drug and reduce the cost and times for drug research. Here we present an overview of SBDD used in drug discovery and highlight its recent successes and major challenges to current SBDD methodologies.

Molecular networks represent the interactions and relations of genes/proteins, and also encode molecular mechanisms of biological processes, development and diseases. Among the molecular networks, protein-protein Interaction Networks (PINs) have become effective platforms for uncovering the molecular mechanisms of diseases and drug discovery. PINs have been constructed for various organisms and utilized to solve many biological problems. In human, most proteins present their complex functions by interactions with other proteins, and the sum of these interactions represents the human protein interactome. Especially in the research on human disease and drugs, as an emerging tool, the PIN provides a platform to systematically explore the molecular complexities of specific diseases and the references for drug design. In this review, we summarized the commonly used approaches to aid disease research and drug discovery with PINs, including the network topological analysis, identification of novel pathways, drug targets and sub-network biomarkers for diseases. With the development of bioinformatic techniques and biological networks, PINs will play an increasingly important role in human disease research and drug discovery.

Molecular docking, as one of the widely used virtual screening methods, aims to predict the binding-conformations of small molecule ligands to the appropriate target binding site. Because of the computational complexity and the arrival of the big data era, molecular docking requests High- Performance Computing (HPC) to improve its performance and accuracy. We discuss, in detail, the advances in accelerating molecular docking software in parallel, based on the different common HPC platforms, respectively. Not only the existing suitable programs have been optimized and ported to HPC platforms, but also many novel parallel algorithms have been designed and implemented. This review focuses on the techniques and methods adopted in parallelizing docking software. Where appropriate, we refer readers to exemplary case studies.