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

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

Molecular property diagnostic suite for diabetes mellitus (MPDSDM): An integrated web portal for drug discovery and drug repurposing.

Machine learning based dynamic consensus model for predicting blood-brain barrier permeability

The drug-resistant strains of Mycobacterium tuberculosis (M.tb) are evolving at an alarming rate, and this indicates the urgent need for the development of novel antitubercular drugs. However, genetic mutations, complex cell wall system of M.tb, and influx-efflux transporter systems are the major permeability barriers that significantly affect the M.tb drugs activity. Thus, most of the small molecules are ineffective to arrest the M.tb cell growth, even though they are effective at the cellular level. To address the permeability issue, different machine learning models that effectively distinguish permeable and impermeable compounds were developed. The enzyme-based (IC50) and cell-based (minimal inhibitory concentration) data were considered for the classification of M.tb permeable and impermeable compounds. It was assumed that the compounds that have high activity in both enzyme-based and cell-based assays possess the required M.tb cell wall permeability. The XGBoost model was outperformed when compared to the other models generated from different algorithms such as random forest, support vector machine, and naive Bayes. The XGBoost model was further validated using the validation data set (21 permeable and 19 impermeable compounds). The obtained machine learning models suggested that various descriptors such as molecular weight, atom type, electrotopological state, hydrogen bond donor/acceptor counts, and extended topochemical atoms of molecules are the major determining factors for both M.tb cell permeability and inhibitory activity. Furthermore, potential antimycobacterial drugs were identified using computational drug repurposing. All the approved drugs from DrugBank were collected and screened using the developed permeability model. The screened compounds were given as input in the PASS server for the identification of possible antimycobacterial compounds. The drugs that were retained after two filters were docked to the active site of 10 different potential antimycobacterial drug targets. The results obtained from this study may improve the understanding of M.tb permeability and activity that may aid in the development of novel antimycobacterial drugs.

Mycobacterium tuberculosis Cell Wall Permeability Model Generation Using Chemoinformatics and Machine Learning Approaches

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 )

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