The scientists, and the researchers around the globe generate tremendous amount of information everyday; for instance, so far more than 74 million molecules are registered in Chemical Abstract Services. According to a recent study, at present we have around 1060 molecules, which are classified as new drug-like molecules. The library of such molecules is now considered as 'dark chemical space' or 'dark chemistry.' Now, in order to explore such hidden molecules scientifically, a good number of live and updated databases (protein, cell, tissues, structure, drugs, etc.) are available today. The synchronization of the three different sciences: 'genomics', proteomics and 'in-silico simulation' will revolutionize the process of drug discovery. The screening of a sizable number of drugs like molecules is a challenge and it must be treated in an efficient manner. Virtual screening (VS) is an important computational tool in the drug discovery process; however, experimental verification of the drugs also equally important for the drug development process. The quantitative structure-activity relationship (QSAR) analysis is one of the machine learning technique, which is extensively used in VS techniques. QSAR is well-known for its high and fast throughput screening with a satisfactory hit rate. The QSAR model building involves (i) chemo-genomics data collection from a database or literature (ii) Calculation of right descriptors from molecular representation (iii) establishing a relationship (model) between biological activity and the selected descriptors (iv) application of QSAR model to predict the biological property for the molecules. All the hits obtained by the VS technique needs to be experimentally verified. The present mini-review highlights: the web-based machine learning tools, the role of QSAR in VS techniques, successful applications of QSAR based VS leading to the drug discovery and advantages and challenges of QSAR.

Applications of Quantitative Structure-Activity Relationships (QSAR) based Virtual Screening in Drug Design: A Review.

Computer-Aided Drug Design topic deals with the application of computer hardware and software to provide solutions at every stage of drug discovery. QSAR methods help in evaluating the data emerging from pharmacology laboratories. The molecular docking methods provide atomic-level information regarding the interaction between drugs and macromolecules. The quantum medicinal chemistry methods help in evaluating the electronic structure, and provide many clues regarding the chemical and biochemical processes associated with drugs. The molecular dynamics methods are useful in modelling the induced fit effects of drug–macromolecule complexes. The virtual screening effort is complementary to many preclinical screening efforts being carried out on lead compounds. Artificial intelligence methods are penetrating the domain of drug discovery in a big way by providing many innovative tools. There is a huge requirement in the application of these pharmacoinformatics techniques at every stage of drug discovery. This includes the efforts in druggable target identification, target validation, target 3D structure prediction, medicinal chemistry, natural product chemistry, pharmacology (in vitro as well as in vivo), pharmaceutics, formulations, drug delivery, drug disposition, preclinical trials, clinical trials as well to study the drug–patient response after the release. In this chapter, many of the scientific concepts behind CADD have been introduced, the computational details of these technologies have been elaborated, and the application of these techniques in drug design have been illustrated.

Computer-Aided Drug Design

Discovery of new antibacterial agents is a never-ending task of medicinal chemistry. Every new drug brings significant improvement to patients with bacterial infections, but prolonged usage of antibacterials leads to the emergence of resistant strains. Therefore, novel active structures with new modes of action are required. We describe a web application called AntiBac-Pred aimed to help users in the rational selection of the chemical compounds for experimental studies of antibacterial activity. This application is developed using antibacterial activity data available in ChEMBL and PASS software. It allows users to classify chemical structures of interest into growth inhibitors or noninhibitors of 353 different bacteria strains, including both resistant and nonresistant ones.

AntiBac-Pred: A Web Application for Predicting Antibacterial Activity of Chemical Compounds.

Computational design of molecules with desired properties has become indispensable in many areas of research, particularly in the pharmaceutical industry and academia. Pharmacophore is one of the essential state-of-the-art techniques widely used in various ways in the computer-aided drug design projects. The pharmacophore modelling approaches have been an important part of many drug discovery strategies due to its simple yet diverse usage. It has been extensively applied for virtual screening, lead optimization, target identification, toxicity prediction and de novo lead design and has a huge scope for application in fragment-based drug design and lead design targeting protein–protein interaction interfaces and target-based classification of chemical space. In this chapter, we have briefly discussed the basic concepts and methods of generation of pharmacophore models. The diverse applications of the pharmacophore approaches have been discussed using number of case studies. We conclude with the limitations of the approaches and its wide scope for the future application depending on the research problem and the type of initial data available.

Pharmacophore Modelling and Screening: Concepts, Recent Developments and Applications in Rational Drug Design

Tuberculosis is still an urgent global health problem, mainly due to the spread of multi-drug resistant M. tuberculosis strains, which lead to the need of new more efficient drugs. A strategy to overcome the problem of the resistance insurgence could be the polypharmacology approach, to develop single molecules that act on different targets. Polypharmacology could have features that make it an approach more effective than the classical polypharmacy, in which different drugs with high affinity for one target are taken together. Firstly, for a compound that has multiple targets, the probability of development of resistance should be considerably reduced. Moreover, such compounds should have higher efficacy, and could show synergic effects. Lastly, the use of a single molecule should be conceivably associated with a lower risk of side effects, and problems of drug-drug interaction. Indeed, the multitargeting approach for the development of novel antitubercular drugs have gained great interest in recent years. This review article aims to provide an overview of the most recent and promising multitargeting antitubercular drug candidates.

https://www.mdpi.com/1420-3049/25/5/1239/pdf

Multitargeting Compounds: A Promising Strategy to Overcome Multi-Drug Resistant Tuberculosis.

Molecular Property Diagnostic Suite (
 $$\text {MPDS}^{\mathrm{TB}}$$
 ) is a web tool (
 http://mpdsosddnet
 ) designed to assist the in silico drug discovery attempts towards Mycobacterium tuberculosis (Mtb) $$\text {MPDS}^{\mathrm{TB}}$$
 tool has nine modules which are classified into data library (1–3), data processing (4–5) and data analysis (6–9) Module 1 is a repository of literature and related information available on the Mtb Module 2 deals with the protein target analysis of the chosen disease area Module 3 is the compound library consisting of 11031 million unique molecules generated from public domain databases and custom designed search tools Module 4 contains tools for chemical file format conversions and 2D to 3D coordinate conversions Module 5 helps in calculating the molecular descriptors Module 6 specifically handles QSAR model development tools using descriptors generated in the Module 5 Module 7 integrates the AutoDock Vina algorithm for docking, while module 8 provides screening filters Module 9 provides the necessary visualization tools for both small and large molecules The workflow-based open source web portal, $$\text {MPDS}^{\mathrm{TB}}$$
 101 can be a potential enabler for scientists engaged in drug discovery in general and in anti-TB research in particular SYNOPSIS: A web-based $$\text {MPDS}^{\mathrm{TB}}$$
 Galaxy tool is developed for assessing therapeutic potential of molecules $$\text {MPDS}^{\mathrm{TB}}$$
 is categorized into Data Library, Data Processing and Data Analysis It can be a potential enabler for scientists engaged in drug discovery in general and in anti-TB research in particular

/pdf/assessing-therapeutic-potential-of-molecules-molecular-1rtfzgg6fq.pdf

Assessing therapeutic potential of molecules: molecular property diagnostic suite for tuberculosis $$(\mathbf{MPDS}^{\mathbf{TB}})$$ ( MPDS TB )

Developing effective inhibitors against Mycobacterium tuberculosis (Mtb) is a challenging task, primarily due to the emergence of resistant strains. In this study, we have proposed and implemented an in silico guided polypharmacological approach, which is expected to be effective against resistant strains by simultaneously inhibiting several potential Mtb drug targets. A combination of pharmacophore and QSAR based virtual screening strategy taking three key targets such as InhA (enoyl-acyl-carrier-protein reductase), GlmU (N-acetyl-glucosamine-1-phosphate uridyltransferase) and DapB (dihydrodipicolinate reductase) have resulted in initial 784 hits from Asinex database of 435,000 compounds. These hits were further subjected to docking with 33 Mtb druggable targets. About 110 potential polypharmacological hits were taken by integrating the aforementioned screening protocols. Further screening was conducted by taking various parameters and properties such as cell permeability, drug-likeness, drug-induced phospholipidosisand structural alerts. A consensus analysis has yielded 59 potential hits that pass through all the filters and can be prioritized for effective drug-resistant tuberculosis. This study proposes about nine potential hits which are expected to be promising molecules, having not only drug-like properties, but also being effective against multiple Mtb targets.

A QSAR and molecular modelling study towards new lead finding: polypharmacological approach to Mycobacterium tuberculosis.

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Assessing therapeutic potential of molecules: molecular property diagnostic suite for tuberculosis $$(\mathbf{MPDS}^{\mathbf{TB}})$$ ( MPDS TB )

A QSAR and molecular modelling study towards new lead finding: polypharmacological approach to Mycobacterium tuberculosis.