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Available online at ajdhs.com 

Asian Journal of Dental and Health Sciences 
Open Access to Pharmaceutical and Medical Research 

Copyright  © 2022 The  Author(s): This is an open-access article distributed under the terms of the CC BY-NC 4.0 
which permits unrestricted use, distribution, and reproduction in any medium for non-commercial use provided the 

original author and source are credited 
 

 

 

Review on QSAR using Anticancer Drug 

Hemant Ahirwar, Gabbar Kurmi, Rubeena Khan, Basant Khare,  Anushree Jain, Prateek Kumar Jain, 
Bhupendra Singh Thakur* 

Adina College of Pharmacy, ADINA Campus Rd, Lahdara, Sagar, MP, 470001 

Article Info: 
_______________________________________ 
Article History: 

Received 17 Sep 2022      
Reviewed 09 Nov 2022 
Accepted 24 Nov 2022 
Published 15 Dec 2022 

_______________________________________ 
Cite this article as:  

Ahirwar H, Kurmi G, Khan R, Khare B,  Jain A, 
Jain PK, Thakur BS, Review on QSAR using 
Anticancer Drug, Asian Journal of Dental and 
Health Sciences. 2022; 2(4):59-63 

DOI: http://dx.doi.org/10.22270/ajdhs.v2i4.27    

_______________________________________

*Address for Correspondence:   

Bhupendra Singh Thakur, Adina College of 
Pharmacy, ADINA Campus Rd, Lahdara, Sagar, 
MP, 470001 

Abstract 
___________________________________________________________________________________________________________________ 

New drug discovery has been acknowledged as a complicated, expensive, time-consuming, and 
challenging project. It has been estimated that around 12 years and 2.7 billion USD, on average, are 
demanded for a new drug discovery via traditional drug development pipeline. How to reduce the 
research cost and speed up the development process of new drug discovery has become a challenging, 
urgent question for the pharmaceutical industry. Computer-aided drug discovery (CADD) has emerged 
as a powerful and promising technology for faster, cheaper and more effective drug design. Recently, the 
rapid growth of computational tools for drug discovery, including anticancer therapies, has exhibited a 
significant and outstanding impact on anticancer drug design, and has also provided fruitful insights 
into the area of cancer therapy. In this work, we discussed the Qualitative structure activity relationship, 
a computer-aided drug discovery process with a focus on anticancer drugs. 

Keywords: New drug discovery, Computer-aided drug discovery, the Qualitative structure activity 
relationship, Anticancer 

 

 

Introduction 

Up to now, cancer remains a global and serious public health 
challenge. It is estimated that there are more than 200 
different types of cancer, generally named according to the 
tissue where the cancer was recognized for the first time. 
Cancer is considered to be one of the significant causes for 
death in the 21st century and the most critical obstacle for the 
increase of global life expectancy. According to an analysis by 
the world health organization (WHO) in 2015, cancer is the 
second leading cause of death for patients younger than 70 
years old in 91 countries and the third or fourth leading cause 
of death among 22 other countries1. moreover, a global 
increase of 18.1 million new cancer  cases and 9.6 million 
cancer-related deaths have been reported in a previous study2,  
especially 70% of the death caused by cancer occur in low-
income and middle-income countries. The fast growth of the 
cancer incidence and mortality has turned out to be global 
health challenges. How to reduce the cancer-related death rate 
has attracted significant attention from the government, 
society, medical industry, as well as scientific communities, 
expecting the rapid development of effective and safe drugs 
for cancer treatment. Despite of the impressive progress in 
biotechnologies and further understandings of the disease 
biology, the development of new, practical and innovative 
small molecule drugs remains an arduous, time consuming 
and expensive project, which requires collaborations from 
many expertise in  multidisciplinary fields, including medicinal 
chemistry, computational chemistry, biology, drug 
metabolism, clinical research, etc. Furthermore, it has been 

illustrated that the successful discovery and development of a 
new drug costs 12 years, and expensive investment3. Thus, 
novel drug development strategies with a reduced cost of time 
and money, as well as an enhanced efficiency are in high 
demand, which would contribute to a significant improvement 
in global health and life expectancy. Since the successful 
development of HIV protease inhibitor Viracept in the USA in 
1997, which was the first drug design fully driven by its target 
structure4, computational methods have served as an essential 
tool in drug discovery projects and have been a cornerstone 
for new drug development approaches. This makes the drug 
developmental process faster and cheaper. Recently, the fast 
growth in computational power, including massively parallel 
computing on graphical processing units (GPUs), the 
continuous advances in artificial intelligence (AI) tools5,6, have 
translated fundamental research into practical applications7 in 
the drug discovery field. This attracted considerable attention 
for their outstanding performance on providing new 
promising perspectives and solutions to overcome life 
threatening diseases. In this review, we aim at providing an 
overview of QSAR-method and anti-cancer therapy discovery 
in particular. We reviewed some of the most representative 
examples and clarified fundamental principles by exploring 
studies on anticancer drug designs with the help of QSAR 
methods.  

Anti-cancer drug target prediction  

Human contains approximately 30,000 genes, among which 
around 6,000 to 8,000 sites are estimated as potential 
pharmacological targets. However, less than 400 encoded 

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proteins have been proved to be effective for drug 
development until now8,9. Cancer, compared to many other 
human diseases, now has a plethora of potential molecular 
targets for therapeutic development10. Traditional drug 
discovery mainly follows the paradigm of "one molecule - one 
target - one disease", without considering the interactions 
between drugs and proteins. However, an important fact that 
many complex diseases are relevant to a variety of target 
proteins11-13 has been overlooked. Furthermore, unexpected 
drug functions derived from off-targets are accidental and 
uncontrollable activities because of the ‘poly pharmacological’ 
properties of certain drugs, which might result in undesirable 
side effects. Those are particularly pronounced for cancer 
drugs. On the other hand, there are some positive examples 
that benefit from the different pathways targeted by one given 
molecule. For example, sildenafil (viagra) was developed to 
treat angina, but now it is used for erectile dysfunction 
therapy14. There are several drugs, including anticancer drugs, 
whose corresponding target proteins (both primary and non-
target) remain yet unidentified or unknown15. Furthermore, 
some attractive and potentially effective cancer targets remain 
outside of the scope of pharmacological regulation. Some of 
these targets such as phosphatases, transcription factors, and 
RAS family members have been described as undruggable, as 
they lack effective enzymatic active sites10. To make the full 
use of known drugs to treat new indications, the 
characterization of all potential new ligand binding sites has 
been illustrated as a key point in drug repositioning and 
repurposing. Therefore, new and highly qualitative 
bioinformatic target prediction methods are required for the 
accurate prediction of drug targets. Up to now, a wide range of 
drug target interactive web servers has been established, 
providing a series of drug-target databases and prediction 
tools. Moreover, various computational approaches have been 
used to study potential interactions between proteins and 
drugs. In particular, network-based models and ML-based 
models have emerged as important tools. A review by Chen et 
al. summarizes several available computational models for this 
application13.  Interestingly, a method proposed by Campillos 
et al. that uses the similarity of drug side effects to determine 
whether multiple drugs could interact with the same target 
proteins attracted our attention16. Based on this research, 
Takarabe et al. took advantage of the US FDA’s adverse event 
reporting system (AERS) to define the pharmacological 
similarity of all potential medicines and developed a novel 
system to predict large-scale interactions between unknown 
drug targets15. Notably, AERS was employed to predict 
interactions between drugs and targets for the first time. In 
2010, Klipp et al. summarized several available computational 
models for network-based drug-target prediction17. Moreover, 
various biological data settings, including structures of 
bioactive compounds, sequences of target proteins, and 
information of ligand-target interactions, have been combined. 
A series of machine learning-based approaches have been 
demonstrated as efficient tools in detecting relationships 
among drug structures and corresponding target proteins 
from a large amount of data, such as supervised learning 
method18, bipartite graph learning method19, bipartite local 
model20and so on. A recent review by Mayr et al. compared the 
predictive performance of deep learning with other prediction 
approaches for multiple drug targets in the comparative 
studies of composite target prediction methods. As a result, 
feed-forward neural networks were identified with better 
performance in drug target prediction than other methods21. 
As above, since a large number of compounds and vigorous 
efforts are abandoned and wasted due to the off-target effects 
during the classical drug development procedure, a greatly 
enhanced development of target prediction in new drug 
exploration exhibited attractive advantages and further 
expansion in this area are still highly desirable. 

Quantitative structure activity relationship  

QSAR (Quantitative Structure Activity Relationship) is a 
ligand-based approach that relies on analyzing the biological 
activities of drugs using various molecular descriptors (MDs) 
or fingerprints (FPs). These models mathematically describe 
how the activities response to the targets according to the 
ligand's structural characteristics. QSAR was obtained by 
calculating the correlations between the properties of the 
ligand binding agent and the biological activity measured by 
experiments. Different ML and deep learning (DL) approaches 
have also been applied to develop QSAR models22: including 
Support Vector Machine (SVM), Random Forest (RF), 
Polynomial Regression (PR), Multi Linear Regression (MLR), 
Artificial Neural Network (ANN). Unlike the pharmacophore 
models, QSAR models can measure biological activities 
quantitatively and can even find positive or negative effects 
according to certain characteristics of the molecule on its 
activity. QSAR has been applied to many other molecular 
design purposes, such as predicting the new molecule analog 
activity, optimizing lead, and predicting new structural leads 
in drug discovery. In the classical 2D-QSAR approaches, the 
biological activity is related to physical and chemical features 
consisting of steric, electronic, and hydrophobic characters of 
drugs, and the relationships are represented as mathematical 
equations23. More advanced 3D-QSAR approaches, such as 
comparative molecular field analysis24 and molecular 
similarity indexes in a comparative analysis25, are based on 
the force field calculations. The structural information of 
molecules is needed, and developed models are represented in 
3D contour maps facilitating the visualization and 
interpretation 

Successful stories of computational drug 
discovery  

QSAR methods have proved to play an essential role in 
modern drug discovery. Since computational methods could 
cover almost all stages of the drug discovery pipeline, the 
applications of QSAR methods in anticancer drug discoveries 
have shown great advantages in terms of the required 
investment, resources, and time. More recently, QSAR methods 
have become a potent and powerful tool in several successful 
cases of anticancer drug development. Herein, we list several 
successful applications of QSAR methods for small molecule 
drugs, which have been applied to cancer treatment or are at 
later stages in the clinical trial. In a study of alomori et al., four 
oxidovanadium(IV) complexes have been synthesized and 
characterized by several spectroscopic methods. The IR 
spectra suggested that the ligands have bidentate coordination 
mode to the vanadium ion. In addition, the molar conductance, 
EPR, magnetic moment values, and electronic data support a 
square pyramidal structure for all complexes. 

Computational studies were applied to demonstrate the 
optimization geometry and essential quantum parameters and 
confirm their biological efficiency. The metal complexes 
showed a square-pyramidal geometry arrangement around 
the metal ion which was agreeable with experimental results. 
The form coordination bonds length presented strength 
bonded between oxidovanadium(IV) and the investigated 
ligand to form stable complexes. The bioactivity study was 
varied and involved DNA-binding, molecular docking, QSAR, 
and cytotoxicity analyses. DNA binding results revealed two 
behaviors for the synthesized oxidovanadium(IV) complexes 
with an increase in CT-DNA concentration, hyperchromic with 
electrostatic or grooves binding modes and hypochromic 
signifying an intercalation binding mode. Molecular docking 
results showed that the [VO (CTZ)2] 2H2O complex exhibited 
significant interaction with colon cancer (3IG7) Protein with 
good selectivity. QSAR study has given significant information 



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on biological activity by using the MLR method. QSAR model 
showed a good correlation between the predicted and the 
experimentally observed inhibitory activities. The results of 
validation indicate that the generated QSAR model possessed 
a high predictive power (R2 = 0.97). Based on the molecular 

docking and QSAR results, [VO(CTZ)2] 2H2O was selected and 
tested for its inhibitory activity against colon cancer cell line 
(HCT116). The selected complex showed higher anticancer 
activity than the standard cisplatin chemotherapy drug26.

 

 

 

In an another research QSAR and QAAR studies have been 
conducted on diverse benzamide-derived HDAC3 inhibitors as 
the first initiative to explore the designing strategies of higher 
active and selective HDAC3 inhibitors over HDAC1 and 
HDAC2. QSAR models reveal that molecular size and shape 
along with the steric effect should have to be optimized to 
achieve higher HDAC3 inhibition. QAAR models reflect that 
modification/substitution at the benzamide scaffold should be 
optimized in such a way so that these molecules possess lower 
steric bulk along with nonpolar features for achieving higher 

HDAC3 selectivity over HDAC1 and HDAC2. However, the 
importance of spiro hydrophobic cap group, as well as 
electron withdrawing fluorine group at the benzamide 
scaffold, should be well-accounted for retaining higher HDAC3 
selectivity over HDAC1. Moreover, less polar and less 
hydrophobic benzamides are preferred for HDAC3 selectivity 
over HDAC2. This detailed structural exploration will surely 
unveil a new vista of designing highly potent and selective 
benzamide-based HDAC3 inhibitors that may be a crucial 
weapon to battle against a variety of cancers27. 

 

 



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An example of QSAR studies, to design new molecules with 
anticancer activity, two 3D QSAR models was developed: 
CoMFA (Q2 = 7.32, R2 = 9.92) and CoMSIA (Q2 = 7.32, R2 = 
9.92) based on 42 Pyrazole derivatives. The results obtained 
for both models were satisfactory and validated by reliable 
methods. All the newly designed molecules respect the 
different drug-likeness rules (Lipinski, Veber, Ghose, Egan, 
Muegge, and ADMET prediction). Moreover, molecular 

docking studies results on EGFR receptor indicated that the 
interaction results of the designed compounds T1-T5 in the 
cavity of the EGFR receptor (1 M17) show more type and the 
number of interactions (Hydrogen Bond interaction) 
compared to erlotinib as reference inhibitors of EGFR. 
Together, these results facilitate and guide the design and 
synthesis of novel and more potent Pyrazole derivatives with 
anticancer activity28. 

 

 

Conclusion 

Cancer has become a tangible threat to human health. About 
9.6 million people are estimated to die from the various forms 
of cancer each year, according to a statistic report. Cancer has 
become the second-largest disease that causes human death. 
However, developing a new drug molecule costs 12 years and 
2.7 billion USD on average. The drug development for cancer 
even becomes more complicated, especially considering the 
molecular pharmacology is still not well understood. Hence, 
the discovery and development of new drugs is considered 
very expensive and time-consuming. In this respect, 
computational methods could be constructive for performing 
different tasks including protein-interaction network analysis, 
drug target prediction, binding site prediction, virtual 
screening, and many others. All these innovative methods 
could considerably facilitate the anti-cancer drug discovery. In 
recent years, with the advance of AI, more sophisticated 
methods, such as QSAR, retro-synthetic routine plan, drug 
scaffold generation, drug binding affinity predictions, were 
developed. The useful predictions generated by QSAR models 
combined with experimental validations could further speed 
up the anti-cancer drug development. 

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