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Special Issue Submission
"Combinatorial Chemistry & High Throughput Screening is a pivotal journal in the field of drug discovery."
Norman R. Farnsworth
Univ. of Illinois at Chicago, USA
Novel applications of graph theory in Chemistry and Drug designing
Guest Editor(s): Jia-Bao Liu
Submit Abstract via Email
Novel Bioinformatics and Systems Biology approaches and techniques in Pharmaceutical and Biochemical Sciences
Guest Editor(s): Yudong Cai
Tentative Publication Date: December, 2016
In silico Methodologies Applied to Drug Discovery
Combinatorial Chemistry & High Throughput Screening, Volume 21, Number 3
Guest Editor(s): Luciana Scotti, Jahan Ghasemi, Marcus T. Scotti
I would like to thank you, Dr. Bijo Mathew and Bentham Science Publishers for providing help in all the steps of the publication process. It was a very good experience for me to work with Bentham Science Publishers due to your prompt replies to my queries. Thanks for everything.
2 Abstract Ahead of Print are available electronically
142 Articles Ahead of Print are available electronically
Drug treatment is a patron saint of human health. It was conservatively estimated that the total revenue
of the worldwide pharmaceutical market exceeded 1.2 trillion U.S. dollars and the global expenditures in
the pharmaceutical research and development (R&D) totaled 179 billion U.S. dollars in the year of
Both the pharmaceutical market and the R&D expenditures are continuously increasing. In spite of
this, a large number of diseased individuals in the world were still untreated promptly without efficient
drugs every day, to die. This is mainly because drug discovery and development does not keep up with
disease evolution, especially viruses. It would take about 14 years and 800 millions of U.S. dollars to
bring a new drug of de novo design to the market by the traditional chemical synthesis [1, 2]. On the
other hand, diseases, especially cancers and viral flus are showing a globally explosive trend. The
number of diseased or infected individuals is remarkably increasing, and new types of disease including cancers are emerging.
For example, the severe acute respiratory syndrome (SARS) coronavirus outbreak in 2003, and the Middle East Respiratory
Syndrome (MERS) coronavirus in 2018 led to the infection of numerous individuals, some of whom died due to the lack of
efficitive drugs. The new coronavirus disease (COVID-19) outbreaking in December 2019 has infected more than 230,000
individuals, involving more than 100 countries as of March 20, 2020 (https://www.who.int/emergencies/diseases/novelcoronavirus-
2019/situation-reports), which was only three months past from the first report of the COVID-19 case. Therefore, it
is one of the challenging tasks that the global scientists and the engineers are jointly faced with how to improve the efficiency
of the pharmaceutical R&D. The advance in the genomics sequencing techniques allows in-depth peep into perspectives of
spatio-temporal activity of cell as well as metabolism of drugs in the cell [3-6]. Therefore, the next-generation sequencing databased
pharmaceutical R&D is becoming a new avenue of drug discovery and development. The computational drug
repositioning is a hot topic of the new pharmaceutical R&D. Compared with the traditional drug discovery and development,
the computational drug repositioning is of low side-effect risk, low investment and short R&D cycle. The past decades have
witnessed the vast achievement of the computational drug repositioning in the drug discovery and development [7-10]. The
thematic issue is intended to provide a forum to collect the latest advances related to computational drug repositioning.
Drug target interactions play a key role in the drug discovery and development. It is by targets which were directly
associated with a disease or regulated the downstream disease-causing genes that drugs impose its interference on the
pathological processes . Identifying drug targets is helpful not only to know the mechanism of drug action , but also to
identify potential adverse side-effects of drugs  and unexpected drug therapy (i.e., drug repositioning) [14, 15]. There are
many types of drug targets, such as G Protein-coupled receptor, Protein kinases, Enzymes, Ion channels, and Transporters. A
drug might simultaneously interact with multiple types of targets. Che et al.  proposed a new computational model to
identify drug target groups. They firstly constructed the drug-drug network by using the chemical-chemical interactions in the
STITCH database , then extracted drug feature by using the network embedding algorithm Mashup , and finally
classified drugs into target groups by using the trained support vector machine  (SVM) classifier. The method is beneficial
to evaluate the behavior of drugs.
Side-effects refer to adverse therapeutic phenotypes after drug treatment . The drug side-effects are closely associated
with drug discovery and development because most new drugs are tested or evaluated for side-effects prior to being brought to
the market . The reason why and the way how drug side-effect is generated are not clear so far. Zhou et al.  investigated
the associations between side-effect and drug substructure. Zhou et al.  built a relationship score between side-effect and
substructure using the chemical-chemical interactions in the STITCH database , which was tested for finding statistically
significant substructure given side-effect. The method enriches drug discovery and development in the evaluation of sideeffects.
Leukemia is a type of blood cancer, which clinically has four subtypes: acute lymphoblastic leukemia, chronic myelogenous
leukemia, acute myeloid leukemia and chronic lymphocytic leukemia [23, 24]. Lu et al.  presented a computational model
to investigate biochemical mechanisms of drugs related to four types of leukemia. The gene ontology (GO) terms  and
Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways  were firstly used to represent leukemia drugs. The
minimum redundancy maximum relevance algorithm  was then used to rank GO terms and KEGG pathways from most to
least relating leukemia drugs. The method would help discriminate biochemical mechanism of four types of leukemia drugs and
are helpful for discovery and development of new leukemia drugs.
Lysine malonylation is a type of protein post-translational modification (PTM), which refers to a process of malonyl group’
covalently modifying the ε-amine group of lysine residue . The PTMs might regulate pathological process of diseases and
thus, are feasible to become therapeutic targets of drug treatment . For example, the malonylation is responsible for type 2
diabetes . Therefore, identifying malonylation sites is crucial to drug design of type 2 diabetes. Wang et al.  presented a machine learning-based method for predicting malonylation sites. In this method, 941 features were used to represent
conservation, amino acid composition, physiochemical and biochemical properties and disorder of residues, and the random
forest  was used as a learning algorithm.
Retinal images provide an enriched view of such eye diseases as diabetic retinopathy, glaucoma and macular degeneration
[34, 35]. The analysis of retinal images can assist in detecting retinal abnormalities incurred potentially by cardiovascular and
retinal disorders. Zhang et al.  proposed a method based on texture features in analyzing and mining information from
retinal images. By quantifying and analyzing textural features of retinal images, they identified the retinal region and improved
the visualization of the retinal blood vessels. This method is helpful for visualizing and examining retinal blood vessels in
clinical practice, and is applicable to computer-aided visual examination of retinal images for the diagnosis of eye diseases.
Protecting proteins and DNA from oxidative stress , the antioxidant proteins are generally responsible for the prevention
of aging-related diseases, such as Alzheimer's disease  and Parkinson's disease . It is no doubt that the knowledge about
the antioxidant protein is helpful to design drugs of these diseases above. Xu et al.  presented a computational method to
predict the antioxidant protein. The amino acid compositions and 9-gap dipeptide compositions were used to encode protein,
the principal component analysis was for extracting informative feature and the SVM  for the classifier.
Genome of each living individual is unique and differs from that of any others, and the variations in the genome are closely
linked to phenotype including physical appearance, susceptibility to disease and response to drug treatments . The
variations of the genome might fall into different categories, such as single nucleotide polymorphisms (SNPs), insertion and
deletion (INDEL) polymorphisms . Li et al.  developed a package of software to simulate multi-variation of Cancer
genomes including SNPs, INDEL, and structural variations. The package is beneficial to detect cancer genome mutations.
Precision diagnosis and personalized therapy have been a dream for a long time, but now with the
technological development, they have become possible. We can early detect complex diseases not just
based on their phenotypes but also based on their molecular characteristics, such as genotype, gene
expression and metabolite. We see human body at an unprecedented resolution. The multi-omics data
helps us to better understand the human system and better intervene the dysfunction. We are at the best
time for medicinal research.
To accelerate precision diagnosis and personalized therapy, we organized this special issue to
introduce the latest developments.
He et al. collected the gene expression profiles from 265 tumor tissues of stage I patient from The Cancer Genome Atlas
(TCGA) databases. Using Cox regression model, they evaluated the association between gene expression and the overall survival
time of patients adjusting for gender and age at the initial pathologic diagnosis. 15 genes were significantly associated with overall
survival time of patients. Their results were helpful for prediction of the prognosis and personalized cancer management .
Xu et al. analyzed clinical data prospectively collected from 760 infertile patients visiting the second Affiliated Hospital and
Yuying Children’s Hospital of Wenzhou Medical University between June 1, 2016 and December 31, 2017. They found that
the fastest clinical pregnancy declining age was between 25 and 32, and female infertility type was evidenced as another key
predictive factor for the cumulative outcome of Assisted Reproductive Techniques (ART) .
An et al. collected 100 patients with Autoimmune Hepatitis (AIH) and 100 healthy volunteers. The levels of IL-17, IL-6,
IL-21 and TNF-α in peripheral blood of all the subjects were detected by enzyme-linked immunosorbent assay and the
frequency of Th17 cells and Treg cells was detected by flow cytometry. They found that Th17 cell frequency and their related
factors IL-17 and TNF-α, were associated with the liver damage, which might be used to monitor AIH disease severity .
Chen et al. analyzed the miRNAs in normal and Non-Small Cell Lung Cancer (NSCLC) patients. They found that miR-330-
3p was significantly up-regulated in NSCLC cell lines and tissues and miRNA-205 was significantly down-regulated in
NSCLC cell lines and NSCLC tissues. miR-330-3p promoted cell invasion and metastasis in NSCLC probably by promoting
EMT and miR-205 could restrain NSCLC, which was likely, by suppressing EMT .
Kataria et al. investigated the synthesized compounds for their antioxidant and in vitro inhibition activity against the jack
bean urease enzyme. They also carried out molecular simulations to determine the complete interaction of the newly
synthesized compounds within the protein pocket. The lead molecules they identified may be useful in treatment of serious
pathogenic conditions caused by urease enzyme .
Zhao et al. constructed a deep learning model to diagnose lung adenocarcinoma. Compared to the traditional methods which
take a single gene as a feature, the relative difference between gene pairs was a higher order feature. Using high-order features
to build the model can avoid instability and make the prediction results more reliable .
Zhong et al. constructed a machine learning model that can effectively predict not only the risk of recurrence of lung cancer
patients, but also the survival of patients. Their model was tested on several datasets and all showed good performance .
Xu et al. reported a case of Intrahepatic Biliary Cystadenoma (IBC) located in caudate lobe and described a typical
procedure of misdiagnosing this disease. The experience and lessons of misdiagnosis in this case may help other clinicians in
the accurate diagnosis of the rare disease .
Shen et al. reported a rare case of pancreatic tumor with duodenal obstruction accompanied by obstructive symptoms, which
was finally confirmed by laparotomy. This case indicated that radical operation appeared to be the first-choice treatment for
patients with malignant duodenal obstruction .
This special issue covered disease detection, drug development and patient therapy. It illustrated the representative scenarios
in precision medicine and could be a good reference for physicians and researchers.
With the development of Next Generation Sequencing (NSG) technologies and advanced deep learning
methods, precision medicine has become actionable. Many diseases can be detected much earlier than traditional
diagnosis methods. Based on patients’ molecular fingerprint, a personalized treatment can be formulated. Big
advances have been achieved, such as the inhibitors of PD-1 (programmed cell death protein 1) approved by the
FDA which can treat various cancers based on patients’ microsatellite instability (MSI).
To facilitate precision medicine, efforts from different areas are needed. In this special issue, we included the
latest precision medicine applications in different areas.
Zhang et al. investigated whether laparoscopic resection is non-inferior to open resection in treatment
outcomes of rectal cancer after neo-adjuvant chemo-radiotherapy. A total of 6 trials were analyzed and they found
that laparoscopic surgery correlated with a longer operative time but a shorter hospital stays, without superior advantages in short-term
survival rates or oncologic efficiency for locally treating advanced rectal cancer after neoadjuvant chemo-radiotherapy .
Huang et al. analyzed twenty-nine randomized control trials (RCTs) which included 1061 patients in the invasive-noninvasive sequential
ventilation group (In-non group) and 1074 patients in the invasive ventilation group (In group). Their results demonstrated that the application
of noninvasive sequential ventilation after invasive ventilation at the pulmonary infection control window has a significant influence on
ventilator-associated pneumonia (VAP) incidence, mortality and length of stay in the ICU .
Hu et al. compared the efficacy and safety of robot-assisted thoracic surgery (RATS) lobectomy versus video-assisted thoracic surgery
(VATS) for lobectomy in patients with non-small cell lung cancer (NSCLC). They discovered that RATS resulted in better mortality as
compared with VATS. But RATS seemed to have longer operative time and higher hospital costs .
Dai et al. analyzed 1186 lower rectal cancer patients from 10 studies, which included 646 laparoscopic and 540 open surgery patients.
They compared the surgical outcomes of laparoscopic surgery and open surgery. They found that both the surgeries had similar operation
time, but the use of the laparoscopic surgery was preferred due to less time in solid intake, short hospital stay time, limited blood loss and
lower complication rate .
Ye et al. collected the publications on comparison of video-assisted thoracoscopic surgery (VATS) versus open thoracotomy for nonsmall
cell lung cancer (NSCLC) patients from 2007 to 2017. A systematic literature search was conducted including 15 studies. Comparing
lobectomy with thoracotomy, thoracoscopic lobectomy was associated with a lower incidence of major complications, including lower rates
of prolonged pneumonia, atrial arrhythmias and renal failure. Lobectomy via VATS may be the preferred strategy for appropriately selected
NSCLC patients .
Liu et al. investigated the effect and mechanism of Hairy Calycosin on non-alcoholic fatty liver dieases (NAFLD) in rats. They
discovered that Hairy Calycosin can effectively control the lipid peroxidation in liver tissues of rats with NAFLD, reduce the levels of serum
TNF-α, IL-6, MDA and FFA, effectively improve the steatosis and inflammation of the liver tissue, and down-regulate the expression of
CYP2E1, thus inhibiting apoptosis of hepatocytes .
Lian et al. detected the expression of LncRNA MINCR and mRNA CDK2 in seventy-five surgically resected primary hepatocellular
carcinoma tissues and adjacent tissues. The expression of LncRNA MINCR and mRNA CDK2 in primary hepatocellular carcinoma tissues was
observed to be higher than that in adjacent tissues. They played a synergistic role in the invasion, and metastasis of hepatocarcinoma cells .
Ye et al. measured the MADD expression levels in normal human lung and human lung adenocarcinoma tissues using
immunohistochemistry. They found that MADD expression was significantly upregulated in lung adenocarcinoma tissue and it can promote
lung adenocarcinoma cell growth by inhibiting apoptosis .
The studies in this special issue showed how powerful the precision medicine was and how to apply precision medicine in clinical
practice. We hope it will inspire more doctors and scientists to perform precision medicine researches.
With the development of the high-throughput technologies, such as Next Generation Sequencing
(NSG) and deep learning analysis of images, precision medicine is no longer a dream and the early
diagnosis of complex diseases, such as cancers and pulmonary diseases, become possible. Not only
precision diagnosis but also precision treatment have achieved huge successes. The inhibitors of PD-1
(programmed cell death protein 1) has approved by the FDA can treat various cancers. The cancer
types are irrelevant, only the mutation pattern matters. It revolutionizes the treatment of diseases.
With all these exciting developments of early and accurate diagnosis using liquid biopsy and
personalized immunotherapy using targeted inhibitors, the underlying image analysis, sequencing
analysis and statistical analysis are the foundation. In this special issue, we included precision
medicine studies using various methods.
Yu et al. studied the association between vitamin D receptor (VDR) genetic polymorphism and lung cancer risk. Four
positions on VDR gene, namely ApaI (rs7975232), BsmI (rs1544410), FokI (rs10735810) and TaqI (rs731236), were
investigated. ApaI and FokI showed no associations while BsmI and TaqI were associated with lung cancer risk .
Ru et al. evaluated the efficacy and toxicity of anti-PD1 to chemotherapy in patients with non-small-cell lung cancer.
Among patients with advanced NSCLC, were observed greater survival benefit, with a favorable safety profile with anti-PD1
than with docetaxel .
Ruan et al. evaluated the efficacy and toxicity of bevacizumab plus chemotherapy compared with bevacizumab-naive based
chemotherapy as second-line systemic therapy in people with metastatic colorectal cancer (CRC). Their results suggest that the
addition of bevacizumab to the chemotherapy therapy could be an efficient and safe option for patients with metastatic
colorectal cancer as second-line treatment and without increasing the risk of adverse event .
Xu et al. reported the rate of adverse event after 1-year follow-up of coronary artery disease (CAD) patients who received
percutaneous interventions (PCI) treatment. The rates for target vessel failure (TVF), target vessel revascularization, target
lesion revascularization, myocardial infarction and major adverse cardiac events were 8.5%, 4.1%, 4.2%, 2.0%, 8.7%,
respectively. The results were useful for post PCI treatment adverse event prevention .
Guo et al. screened genes that were significantly associated with drug resistance of lung cancer patients. They constructed a
diagnostic classification model using the expression level of five genes as the feature and the prediction accuracy reached 85%
Yuan et al. enrolled 120 children who were hospitalized in The First Hospital of Huzhou between January and December
2016 for respiratory tract infection due to M. pneumoniae. Nearly 90% of the resistant M. pneumoniae strains showed A to G
substitution at position 2063 of the 23S rRNA gene .
Xu et al. evaluated the serum ORM1 level in the resistance of EGFR-TKI and optimized the cut off value of ORM1 for the
diagnosis of EGFR-TKI resistance. When compared to those before treatment, the AUC of serum ORM1 concentration was
0.880 ± 0.038 with sensitivity of 92.9% and specificity of 73.8% in the resistance group. The cutoff value of serum ORM1 was
1.778 μg/ml for advanced EGFR-positive LUAD and 1.723 μg/ml after resistance to EGFR-TKI .
Zhang et al. applied several advanced computational methods, such as minimum redundancy maximum relevance (mRMR),
incremental forward search (IFS) and random forest (RF) to investigate cereal hull color at metabolic level. A total of 158 key
metabolites were found to be useful in distinguishing white cereal hulls from colorful cereal hulls. Their results provided new
insights into the molecular basis of complex traits .
Wang et al. developed a new computational pipeline to identify the Driver Mutation-Differential Co-Expresison (DM-DCE)
modules based on dysfunctional networks across 11 TCGA cancers. Their study sheds light on both cancer-specific and crosscancer
characteristics systematically .
Cai et al. compared the modified transverse colostomy with conventional methods. The operation time of stoma
construction was 34±10 minutes for the conventional method and 28±7 minutes for the modified method (P= 0.009). Patients
with conventional transverse colostomy were remarkably more likely to experience parastoma hernia (P= 0.048) and stoma
prolapse (P= 0.038). Overall, the modified transverse colostomy is a safe and effective diverting technique .
Wang et al. proposed a novel network embedding method, which can extract topological features of each drug combination
from a drug network that was constructed using chemical-chemical interaction information retrieved from STITCH. Their
support machine vector (SVM) classifier yielded a Matthews correlation coefficient (MCC) of 0.806 .
Zhu et al. analyzed the medical records and various data of patients with lymphocyte interstitial pneumonia (LIP). They
found that the diagnosis of lymphocyte interstitial pneumonia (LIP) with high-resolution CT can increase the clinical diagnosis
rate, reduce misdiagnosis and improve early detection .
Zhang et al. studied hemolymphangioma, a rare benign tumor. Early diagnosis of hemolymphangioma is difficult, because
its symptoms can be imperceptible for a long time. A case of 30-year-old hemolymphangioma woman patient with 2 years of
follow-up was reported and more understanding of hemolymphangioma was accumulated .
Hu et al. reported a rare case of esophageal cancer maxilla metastasis (ECMM) with the involvement of the right side of the
soft palate and maxillary sinus. They explored the possible mechanisms and predictors of esophageal cancer metastasis .
With these studies, we hope that more and more people will realize the power of advanced analysis in precision medicine
and utilize these methods in their practice.
With the development of Next-Generation Sequencing (NGS) and high throughput omics technologies, the whole picture of
complex diseases and biochemical processes can be revealed. But analyzing these big data and integrating various omics data
are a great challenge for pharmacologists and biologists. Take the TARGET (Therapeutically Applicable Research to Generate
Effective Treatments) project as an example, it provides a comprehensive genomic big data of childhood cancers, including
Acute lymphoblastic leukemia (ALL), Acute myeloid leukemia (AML), Kidney Tumors, Neuroblastoma (NBL) and
Osteosarcoma (OS). It is very difficult to determine the key molecular changes that drive tumorgenesis from millions of
transcriptome profiling, nucleotide variation, copy number variation and clinical data.
Meanwhile, the development of Artificial Intelligence (AI) technologies, such as deep learning methods, is also rapid.
Unfortunatly, the AI scientists who focus on machine learning and graph theory, are not familiar with the biological context of
the great challenges the pharmacologists and biologists are facing. IBM Watson is a good start for introducing AI to analyze
clinical information and medical literature. But we need more and more such inter-discipline studies or projects to overcome
the barrier between computational scientists and pharmacologists/biologists. Together, novel Bioinformatics and Systems
Biology methods and software may be developed to solve the big data analysis and the heterogeneous data integration problem.
Until then, the science community may find the cause of complex diseases and the compounds that may cure the diseases. In
this special issue, we demonstrated the application of novel bioinformatics and systems biology approaches and techniques in
pharmaceutical and biochemical sciences.
Li et al. established the prognostic model for gastric adenocarcinoma based on the six genes that were significantly
correlated with the survival time for patient death risk evaluation. ROC analysis was conducted in the training and validation
datasets, and their AUROC values were 0.774 and 0.723, respectively.
Chen et al. proposed a prediction model that used the extreme learning machine (ELM) algorithm as the prediction engine
to identify nitrated tyrosine residues. This model produced satisfactory results on the training dataset with a Matthew's
correlation coefficient of 0.757. The model was also evaluated by an independent test dataset that contained only positive
samples, yielding a sensitivity of 0.938.
Liu H et al. provided a modified mathematical model of tumor growth with two time delay effects that involve the
interaction between host cells, tumor cells and effector cells in order to understand the dynamic behavior of tumor growth. In
their model, biological relevance was explained and found to be different from the existing methods.
Liu T et al. proposed a novel integrated multi-label classifier with chemical-chemical interactions for prediction of chemical
toxicity effects. By testing the integrated classifiers on a dataset with chemicals and their toxicity effects on Accelrys Toxicity
database and non-toxic chemicals with their performance evaluated by jackknife test, an optimal integrated classifier was
selected as the proposed classifier, which provided quite high prediction accuracies and wide applications.
Wang S et al. presented a hybrid feature selection method mRMR-ICA which combines minimum redundancy maximum
relevance (mRMR) with imperialist competition algorithm (ICA) for cancer classification. The presented algorithm mRMRICA
utilizes mRMR to delete redundant genes and provide the small datasets for ICA. Experimental results including the
accuracy of cancer classification and the number of informative genes are improved for mRMR-ICA compared with the
original ICA and other evolutionary algorithms.
Jiang et al. proposed a new computational method for the identification of novel colorectal cancer (CRC)-associated genes.
The proposed method was based on existing proven CRC-associated genes, human protein–protein interaction networks, and
random walk with restart algorithm. Using the proposed method, they successfully identified 122 novel CRC-associated genes.
Wang C et al. developed an integrated model with five basic components and two time delays for the p53 network. Using
such time delays as the bifurcation parameter, the existence of Hopf bifurcation was given by analyzing the relevant
characteristic equations. Their results indicated that the transcriptional and translational delays can induce oscillation by
undergoing a super-critical Hopf bifurcation and the length of these delays can control the amplitude and period of the
These seven works proved that bioinformatics and systems biology approaches and techniques were very powerful in
pharmaceutical and biochemical sciences. We hope that more and more pharmacologists and biologists will try these
bioinformatics and systems biology methods in their studies.
The term in silico refers to the computational component silicium, indicating in silico methods that are used for experiments
performed by computers. This is a relatively new area, implemented in the mid-80s and until then, is rapidly developing and is
being widely used in industry and academy, especially in the field of drug design, which has contributed to the search for new
bioactive compounds for treating various diseases. The in silico methods are based on an examination of the chemical structure
of compounds, identifying features responsible for specific biological activity, thus enabling the estimation of theoretical data
and the validation of any statistical model of a large set of compounds before they are synthesized, contributing significantly to
accelerating the design/discovery process and reducing the need for expensive lab work and clinical trials [1-3].
Theoretical studies using in silico methods have aided in the process of drug discovery. Technological advances in the areas
of structural characterization, computational science, and molecular biology have contributed to the faster planning of new
feasible molecules. Chemoinformatic studies show that a large fraction of compounds are “drug-like” or at least, “lead-like”
having structural and physicochemical properties that render them as potential drugs or leads. This thematic issue will bring
together theoretical studies of different methodologies, such as QSAR, docking, chemometric tools, artificial intelligence and
other applied in order to optimize the search for new drugs for the cure and treatment of several diseases. Computational
screening of small molecule compounds against protein targets implicated in a disease of interest has been widely used to
discover inhibitors, potentially involving the identification of the successes in the interaction of the systematic chemical group
with a compound already known to inhibit a target, as in quantitative activity structure (QSARs), or by "fitting" a large
molecule into the database of compounds in the active site of the three-dimensional (3D) structure of a target protein based on
the calculated binding affinity of the molecule to the target. As the number of high-resolution protein structures and the
processing power of computers have increased exponentially in recent years, so the methods used in the computational database
to complement experiments with High-Throughput Screening (HTS) methods to improve efficiency and effectiveness of the
discovery of lead inhibitors. In addition, studies have shown that HTS success rates are increased to several folds when
compounds are prefiltered by computational screening [4, 5].
This issue brings reviews concerning various types of theoretical methods in drug discovery. Our manuscript, entitled
“Docking of Natural Products against Neurodegenerative Diseases: General Concepts”, discussed the health benefits that
products provide which have become a motive for the treatment studies of various diseases; among them, the neurodegenerative
diseases, like Alzheimer's, considered as the most common form of dementia, and Parkinson's, a major age-related
neurodegenerative disorder. Studies with natural products for neurodegenerative diseases (particularly through molecular
docking) search for, and then focus on those ligands which offer effective inhibition of the enzymes monoamine oxidase and
acetylcholinesterase. The review introduced the main concepts involved in docking studies with natural products: and also on
our group, which has conducted a docking study of natural products isolated from Tetrapterys mucronata for the inhibition of
The work of the Drs. Suroowan & Mahomoodally, “Herbal products for common auto-inflammatory disorders - Novel
approaches”, discussed novel therapeutic approaches that involve the use of nanoparticles loaded with Zingiber officinale
Roscoe extracts to specifically target the colon in irritable bowel disease. In silico approaches remain also a pertinent avenue to
unveil highly compatible herbal metabolites binding multiple targets involved in inflammation.
Synapsin II regulates neurotransmitter release from mature nerve terminals and plays an important role in the formation of
new nerve terminals. The associations of SYN II have been identified in various studies that are linked to the onset of
Schizophrenia. Schizophrenia is characterized by an abnormal behavior like obsession, dampening of emotions and auditory
hallucination. The review of Drs. Tahir & Sehgal, “Pharmacoinformatics and Molecular Docking studies reveal potential novel
compounds against Schizophrenia by target SYN II”, provided the structural insights which may be used for further
understating of the Schizophrenia therapeutic purposes by targeting SYN II and other inhibitors haunting.
Various currently available antimicrobial drugs are inadequate to control the infections and lead to various adverse drug
reactions. Efforts based on Computer-Aided Drug Design (CADD) can excavate a large number of databases to generate new,
potent hits and minimize the requirement of time as well as money for the discovery of newer antimicrobials. Pharmaceutical
sciences have also made development with advances in drug designing concepts. The present research article focuses on
studying various G-6-P synthase inhibitors from the literature cited molecular database. Docking and ADMET data of various
molecules were obtained by Schrodinger Glide and PreADMET software respectively. In the article of Dr. Lather et al., entitled
Virtual Screening of Novel Glucosamine-6-Phosphate Synthase Inhibitors, the results presented the efficacy of various
inhibitors towards enzyme G-6-P synthase. Docking scores, binding energy and ADMET data of various molecules showed
good inhibitory potential for G-6-P synthase as compared to standard antibiotics.
The manuscript of Dr. Paliwal and co-workers, “Pyrazole Schiff base hybrid as an anti-malarial agent: Synthesis, in vitro
screening and computational study”; provided evidence which implicate the pyrazole schiff base hybrids as potential prototypes
for the development of antimalarial agents.
The aim of the study by Dr. Rastija et al., entitled “QSAR analysis for antioxidant activity of dipicolinic acid derivatives”
was to derive robust and reliable QSAR models for clarification and prediction of antioxidant activity of 43 heterocyclic and
Schiff bases dipicolinic acid derivatives. Statistical performance of two different algorithms for splitting data into training and
test set (randomly and ranking method), as well as models obtained by two set of descriptors, was calculated by different
“In-silico studies of isolated phytoalkaloid against Lipoxygenase: study based on possible correlation” is the work of Dr.
Khan et al., which discussed various alkaloids of plant origin that have already shown lipoxygenase inhibition in-vitro with
possible correlation in in silico studies. Molecular docking studies were performed using MOE (Molecular Operating
Closing this issue, we can find the manuscript of Dr. Khattab and co-authors, entitled “Extraction, identification and
biological activities of saponins in sea cucumber Pearsonothuria graeffei”. The work aimed to separate, identify and test
various biological activities (anti-bacterial, antifungal, antileishmanial and anticancer properties) of saponins produced by the
holothurian Pearsonothuria graeffei from the Red Sea, Egypt.
We, the Guest-Editors, would like to express our gratitude to all the authors who contributed to this special issue, reporting
investigations on various aspects of in silico methodologies applied to drug discovery.
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