Article In Press -Journal of Clinical Research and Analytical Reviews

In Silico Development of Anticancer Drugs

Article Information



Citation: Vikrant Verma (2022) In silico development of anticancer drugs. J Clin Res Analyt Review


Copyright: © 2022 Vikrant Verma. This is an open-access article distributed under the terms of the Creative Commons Attribution License, whichpermits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Publication history: 

Received date: 2022-02-16

Accepted date: 2022-04-15

Published date: 2022-05-02


The development of new drugs has been recognised as a complex, costly, time-consuming, and difficult enterprise. A new medication discovery via typical drugdevelopment pipeline is predicted to take around 12 years and 2.7 billion USD on average. Methods to cut research costs and accelerate the development process of new drugs has become a difficult and pressing topic for the pharmaceutical sector. Computer-aided drug discovery (CADD) has developed as a potent and promising technique for designing drugs that are quicker, cheaper, and more effective. Recently, the fast development of computational techniques for drug discovery, particularly anticancertherapies, has had a substantial and exceptional influence on anticancer medication design, as well as beneficial insights into the field of cancer therapy.This article describes docking of Crizotinib, Sunitinibmalate, and their analogues with the Anaplastic lymphoma kinase receptor.On docking with the Anaplastic lymphoma kinase receptor, Crizotinib and Sunitinibmalate yielded energy values of -9.85 and -8.25, respectively.


Cancer, Arguslab, Lymphoma, Docking, Computational.


Cancer continues to be a global and major public health issue. It is thought that there aremore than 200 distinct forms of cancer, often called according to the tissue where the cancerwas identified for thefirst time. Cancer is regarded as one of the leading causes of mortality in the twenty-first century, as well as the most serious impediment to increasing global life expectancy. Cancer is the second largest cause of mortality for patients younger than 70 years old in 91 countries, and the third or fourth major cause of death in 22 additional countries, according to a 2015 World Health Organization (WHO) report. Furthermore, there has been a global rise of 18.1 million new cancer diagnoses and 9.6 million cancer-related deaths. [1,2]. As a result, innovative drug development methodologies with lower time and money costs, as well as increased efficiency, are in great demand, and would lead to a considerable improvement in world health and life expectancy. Since the successful development of the HIV protease inhibitor Viracept in the United States in 1997, which was the first drug design entirely driven by its target structure, computational methods have been a critical tool in drug discovery projects and a cornerstone for new drug development approaches. This expedites and reduces the cost of medication development [3].

The rapid increase in cancer incidence and death has become a worldwide health problem. The question of how to minimise cancer-related deaths has piqued the interest of the government, society, medical sector, and scientific communities, who are hoping for the speedy development of effective and safe cancer-treatment medications.In this research  an overview is provided related to computational-method-aided novel drug development processes in general, and anti-cancer therapeutic discovery in particular. In this research mainly an anticancer drug designs using computational approach was done.

Material Required

For the current work resources such as PubChem, Drug Bank, and PDB (Protein Data Bank), as well as tools such as Arguslab,Weblab,FROG ADME, molinspiration Toxic Drug Bank are utilized.A bioinformatics or cheminformatics resource that integrates complete drug (i.e. chemical) data with thorough drug (i.e. chemical) data pharmacological target  is made use of(i.e. protein). Each Drug Card entry includesmore than 80 data fields, with half of the information dedicated to drug/chemical data, and the other half to medication target or protein information.The PDB (Protein Data Bank) is the sole global database of structural data for biological macromolecules, founded in 1971 at Brookhaven National Laboratories (BNL). It comprises structural information on macromolecules determined by X-ray crystallography, NMR, and other techniques. Molinspiration is an independent research group dedicated to the development and use of current cheminformatics approaches, particularly those related to the internet. Arguslab has strong on-screen molecule-building capabilities, as well as a moderate library of relevant compounds [4]. It is a free molecular modelling programme for Windows.The application reads molecular coordinate files and displays the molecule on the screen interactively in a number of formats and colour schemes. RASMOL [Raster Display of Molecules] is a molecular graphics tool used to visualise the structural properties of proteins, nucleic acids, and tiny biomolecules. The application reads molecular coordinate files and displays the molecule on the screen interactively in a number of formats and colour schemes.

In pharmacokinetics and pharmacology, ADME/TOX is an abbreviation that stands for absorption, distribution, metabolism, and excretion, and it explains the deposition of a pharmacological chemical within an organism. These four criteria all impact drug levels and the kinetics of drug exposure to tissues, and hence influence the compound's performance and pharmacological activity as a medication (

ACD/ChemSketch is a robust all-purpose chemical drawing and graphics tool developed by ACD/Labs to assist scientists in rapidly and simply sketching molecules, reactions, and schematic diagrams, calculating chemical characteristics, and creating professional reports and presentations. SMILES notations may be converted to structure and vice versa using ACD Chemsketch [5,6,7,8].

Optimization and Preparation of Protein

The RCSB protein data bank ( was used to get the crystal structure of Anaplastic lymphoma kinase (ALK) used in this investigation. Using Accelrys Discovery Studio Visualizer 2.5.5, the missing residues were rectified and the complexes attached to the receptor molecule were eliminated.ArgusLab was used to reduce the energy in the PDB files. Water molecules that were not required were eliminated, and polar hydrogens were fused.

Preparation and Optimization of Ligands

The structures of the medicines and analogues were drawn and their MOLs were created using ChemsketchSoftware.The file was followed by the subsequent development of their 3-D structures utilising the tool Weblab viewer light programme a molecular format converter into PDB. Appropriate force fields were applied to them, and optimization was performed via Argus Lab 4.0 ( Argus lab docking software was used to analyse the docking of Crizotinib, Sunitinib malate, and their analogues with the Anaplastic lymphoma kinase receptor [9].

Docking Analyzing

Argus lab docking software was used to analyse the docking of Crizotinib, Anaplastic lymphoma kinase receptor, and their analogues with Anaplastic lymphoma kinase receptor.

Docking enables scientists to digitally examine a library of chemicals and estimate the strongest binders using various scoring algorithms. It investigates how two molecules, such as medicines and an Anaplastic lymphoma kinase receptor, fit together and dock properly. Docking was used to identify Crizotinib, Sunitinibmalate, its derivatives, and receptor complexes, and their relative stabilities and binding affinities were investigated using molecular dynamics and free energy simulations. By default, all of the Arguslab docking settings are set. According to the literature, the medications Crizotinib and Sunitinibmalate have been used to target the Anaplastic lymphoma kinase receptor. On docking with the Anaplastic lymphoma kinase receptor, Crizotinib and Sunitinibmalate yielded energy values of -9.85 and -8.25, respectively.

Using RasMol, it was discovered that the carbonyl groups present in the drug were the location of binding  of drug to the receptor, and the presence of methyl groups resulted in a drop in energy values. Chemsketch was used to make these changes, and Argus lab was used to determine the energy values [10].

The pharmacophoric component of the medication was therefore partially identified. Docking studies of the medication and its derivatives using Arguslab docking software show that the e-value of crizotinib analogue 2 (-11.21) is better than the original (-9.86) and the e-value of sunitinibmalate analogue 1 (-10.85) is better than the original (-9.86). (-8.26). Using ChemSketch, a virtual analogue containing an extra CH3 atom (crizotinib analogue 2) was created.


Figure 6: Docking of crizotinibanalog 2 with Anaplastic lymphoma kinase receptor using ArgusLab.


ADME and Lipinski's Rule

The analogues were created in 2D at the web portal ( and then submitted to ADME (Absorption, Distribution, Metabolism, and Excretion) study. Hit/lead compounds can be identified utilising high-(medium) throughput screening methods and/or virtual screening simulations. In all cases, a compound collection is screened with the goal of finding molecules that could enter the drug discovery process or that could help to explore molecular mechanisms; however, it is well documented that to avoid costly failures in screening projects, ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties should be considered at an early stage. The analogues were created in 2D using the internet portal ( and then tested using Lipinski's Rule of Five. The drug-likeness of the analogues was assessed using 'The Lipinski Rule of Five,' which is available online at the Supercomputing Facility for Bioinformatics and Computational Biology, Indian Institute of Technology, New Delhi, India. The Lipinski's rule, developed by Christopher A. Lipinski in 1997, is a guideline for determining if a particular chemical molecule with a certain pharmacological, biological, and ADME (absorption, distribution, metabolism, and excretion) activity has the potential to be an orally active medication in humans [9].

Crizotinib (-9.86) and Sunitinib malate-7.77) were the energy values obtained after the receptor was docked with the medicines (-8.26). When the modified medicines were docked at the same receptor, the energy values obtained were crizotinib analogue 2 (-11.21), sunitinibmalate analogue 1 (-11.21), and sunitinibmalate analogue 2 (-11.21). (-10.85)

As a result, it isinferrred that some of the modified medications are superior to commercially available pharmaceuticals on the market. In future study, the ADME/T (Absorption, Distribution, Metabolism, Excretion/Toxicity) characteristics of these chemicals may be studied in a wet lab, and clinical trials can be planned. In the future, this study can be employed in clinical trials to assess its effectiveness and for societal benefit, lowering the time and expense of the medication discovery process.


Cancer has evolved into a real hazard to human health. According to a statistical analysis, around 9.6 million people are predicted to die each year from various types of cancer . Cancer has surpassed heart disease as the second leading cause of mortality in humans. However, the average cost of generating a new medicinal molecule is 12 years and 2.7 billion USD . Drug discovery for cancer is becoming ever more challenging, especially since molecular pharmacology is still poorly understood. As a result, the discovery and development of novel medications is seen as both costly and time-consuming. In this regard, computational approaches might be useful for a variety of tasks such as protein-interaction network analysis, drug-target prediction, binding site prediction, virtual screening, and so on. All of these novel approaches might greatly aid in the discovery of anti-cancer drugs.


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