Current Topics in Medicinal Chemistry - Volume 18, Issue 20, 2018

Background: To carry out wide range of cellular functionalities, proteins often associate with one or more proteins in a phenomenon known as Protein-Protein Interaction (PPI). Experimental and computational approaches were applied on PPIs in order to determine the interacting partners, and also to understand how an abnormality in such interactions can become the principle cause of a disease. Objective: This review aims to elucidate the case studies where PPIs involved in various human diseases have been proven or validated with computational techniques, and also to elucidate how small molecule inhibitors of PPIs have been designed computationally to act as effective therapeutic measures against certain diseases. Results: Computational techniques to predict PPIs are emerging rapidly in the modern day. They not only help in predicting new PPIs, but also generate outputs that substantiate the experimentally determined results. Moreover, computation has aided in the designing of novel inhibitor molecules disrupting the PPIs. Some of them are already being tested in the clinical trials. Conclusion: This review delineated the classification of computational tools that are essential to investigate PPIs. Furthermore, the review shed light on how indispensable computational tools have become in the field of medicine to analyze the interaction networks and to design novel inhibitors efficiently against dreadful diseases in a shorter time span.

The enormous quantity of publicly available active chemical ligand and biological receptor data knowledge allows scientists to retreat several open questions by the analysis and systematic integration of these complex unique data. Systems biology plays a crucial role through the constructive alignment of bio-physiochemical monitoring of gene, protein along with metabolites from the complex data. Further, it integrates information within the data and responses (metabolic and signaling pathway) which lead to the formulation of computational models for the elucidation of structure and function of the molecular determinant. The system biology methods utilize big complex high throughput data for the identification of the whole drug target and for the mechanism of action to lead compound characterization. Nowadays, the system biology is one of the most popular approaches to characterize proteinligand interaction on a large scale and is vital to address a complex mode of the drug action to clinical indications. The network of protein-ligand interactions also reveals the correlation between molecular functions of the cell with their physiological processes which help to design safe and effective ligands for drug development. Here, we review recent attempts to apply system biology-based approaches with large-scale network analyses to predict novel interactions of ligand and targets. We also deliver an essential step involved in the discovery and development of such multi128;target drugs by identifying the group of proteins targeted by a particular ligand, leading to innovation in therapeutic research.

The conventional way of characterizing a disease consists of correlating clinical symptoms with pathological findings. Although this approach for many years has assisted clinicians in establishing syndromic patterns for pathophenotypes, it has major limitations as it does not consider preclinical disease states and is unable to individualize medicine. Moreover, the complexity of disease biology is the major challenge in the development of effective and safe medicines. Therefore, the process of drug development must consider biological responses in both pathological and physiological conditions. Consequently, a quantitative and holistic systems biology approach could aid in understanding complex biological systems by providing an exceptional platform to integrate diverse data types with molecular as well as pathway information, leading to development of predictive models for complex diseases. Furthermore, an increase in knowledgebase of proteins, genes, metabolites from high-throughput experimental data accelerates hypothesis generation and testing in disease models. The systems biology approach also assists in predicting drug effects, repurposing of existing drugs, identifying new targets, facilitating development of personalized medicine and improving decision making and success rate of new drugs in clinical trials.

Protein-ligand interaction is an imperative subject in structure-based drug design and protein function prediction process. Molecular docking is a computational method which predicts the binding of a ligand molecule to the particular receptor. It predicts the binding pose, strength and binding affinity of the molecules using various scoring functions. Molecular docking and molecular dynamics simulations are widely used in combination to predict the binding modes, binding affinities and stability of different protein-ligand systems. With advancements in algorithms and computational power, molecular dynamics simulation is now a fundamental tool to investigative bio-molecular assemblies at atomic level. These methods in association with experimental support have been of great value in modern drug discovery and development. Nowadays, it has become an increasingly significant method in drug discovery process. In this review, we focus on protein-ligand interactions using molecular docking, virtual screening and molecular dynamics simulations. Here, we cover an overview of the available methods for molecular docking and molecular dynamics simulations, and their advancement and applications in the area of modern drug discovery. The available docking software and their advancement including application examples of different approaches for drug discovery are also discussed. We have also introduced the physicochemical foundations of molecular docking and simulations, mainly from the perception of bio-molecular interactions.

Application of computational tools and techniques has emerged as an invincible instrument to unravel the structure-function relationship and offered better mechanistic insights in the designing and development of new drugs along with the treatment regime. The use of in silico tools equipped modern chemist with armamentarium of extensive methods to meticulously comprehend the structural tenacity of receptor-ligand interactions and their dynamics. In silico methods offers a striking property of being less resource intensive and economically viable as compared to experimental evaluation. These techniques have proved their mettle in the designing of potential lead compounds to combat life-threatening diseases such as AIDS, cancer, tuberculosis, malaria, etc. In the present scenario, computer-aided drug designing has ascertained an essential and indispensable gizmo in therapeutic development. This review will present a brief outline of computational methods used at different facets of drug designing and its latest advancements. The aim of this review article is to briefly highlight the methodologies and techniques used in structure-based/ ligand-based drug designing viz., molecular docking, pharmacophore modeling, density functional theory, protein-hydration and molecular dynamics simulation which helps in better understanding of macromolecular events and complexities.

Background: Zika is a worldwide pandemic dreadful viral transmission through Aedes mosquito vector. It significantly causes fever, joint pain or rash, and conjunctivitis. Pregnant mothers suffering from Zika viral infection may have fetal abnormalities due to severe neurological problems, characterized by microcephaly along with Guillain-Barré syndrome, issuing ZIKV a major public health concern as declared by the World Health Organization. There is hardly any FDA approved anti-Zika viral drugs available. Objective: Therefore, it is a big panic for the scientists to destroy the virus completely by generating potent inhibitors. Methods: For the purpose, various Zika viral targets were explored by structure-based design in the present review in connection with the discovery of various synthetic and natural sourced inhibitors against Zika virus. Results: The structure-based drug design tools such as x-ray crystallography and molecular docking reported various co-crystallized ligands and Zika virus inhibitors. Conclusion: Such inhibitors could further be modified for the design of highly active leads to combat Zika virus utilizing chemoinformatics modules.

The intertwining of chemoinformatics with artificial intelligence (AI) has given a tremendous fillip to the field of drug discovery. With the rapid growth of chemical data from high throughput screening and combinatorial synthesis, AI has become an indispensable tool for drug designers to mine chemical information from large compound databases for developing drugs at a much faster rate as never before. The applications of AI have gone beyond bioactivity predictions and have shown promise in addressing diverse problems in drug discovery like de novo molecular design, synthesis prediction and biological image analysis. In this article, we provide an overview of all the algorithms under the umbrella of AI, enlist the tools/frameworks required for implementing these algorithms as well as present a compendium of web servers, databases and open-source platforms implicated in drug discovery, Quantitative Structure-Activity Relationship (QSAR), data mining, solvation free energy and molecular graph mining.

Metabolomics is an analytical approach to metabolism and involves quantitative and comparative analysis of low-molecular-weight metabolites in body fluids or cellular/tissues extracts. Owing to its ability to reveal disease-specific metabolic patterns or metabolic changes produced in response to a therapeutic intervention; it is gaining widespread applications virtually in all aspects of biomedical and pharmaceutical research pertaining to human healthcare management. It has also started playing a strategic role in pharmacological and toxicological research for evaluating therapeutic efficacy/safety of promising drug candidates either alone or in conjunction with other omics tools such as genomics, transcriptomics and proteomics. The metabolic profiling capabilities of nuclear magnetic resonance (NMR) spectroscopy along with pattern recognition methods have successfully been applied for identifying a diagnostic panel of biomarkers, evaluating drug efficacy/safety, screening toxicity and disease mechanism. Particularly, the interest in applying NMR-based metabolomics for the assessment of therapeutic efficacy and safety is increasing among drug researchers and drug regulators owing to its nondestructive, non-selective and minimal sample preparation requirement. On top of this, it offers the potential for high-throughput (i.e. >100 samples a day is attainable) and provides highly reproducible results. In this review, we will discuss some of the recent developments related to NMR based metabolomics followed by some recent literature examples to highlight its potential in (a) the evaluation of therapeutic efficacy and safety of lead discovery compounds, (b) monitoring disease status and recovery after treatment and (c) identification and evaluation of biomarkers of systemic/organ-specific toxicity. Additionally, the review will also highlight its role to facilitate clinical trial testing and improve post-approval drug monitoring.