ISSN (Print): 1386-2073
ISSN (Online): 1875-5402
Volume 21, 10 Issues, 2018
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ISSN (Print): 1386-2073
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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 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.
1 Abstract Ahead of Print are available electronically
9 Articles Ahead of Print are available electronically
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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