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Cheminformatics in Medicinal Chemistry

Call for Papers

Cheminformatics or chemical informatics is a hybrid technology of computers and information technology utilized to solve the underlying problems in medicinal chemistry research. Broadly defined, Cheminformatics encompasses the acquisition, visualization, management, and utilization of chemical information with extensive applications in the current scenario of drug discovery by expediting the development of more potent drugs and reducing both time and money. Cheminformatics in silico techniques are widely employed to solve chemical problems in biotechnology and pharmaceutical research offering a tremendous scope in the field of medicinal chemistry and drug discovery.

In the course of drug discovery and the associated processes thereafter, there is a range of factors which have to be kept in account, especially in the context of recent findings which have included another dimension to the already existing traditional protocol of testing the potency of a new drug on a single target, more specifically the pharmacokinetic, physicochemical, and safety profile. As per this added challenge, the therapeutic outcome of a drug is not determined alone by its activity or reaction on a single target factor, but rather its activity profile across testing, as many compounds are tested on a set of targets or in phenotypic screens generating a tremendous amount of data. Therefore, it is in addressing this problem that the indispensable role of cheminformatics stands out.

Advances in computers and information technology as well as the great improvement of storage capacity have effectively facilitated the storage of real-life compounds and virtual compounds as compound libraries or chemical databases. These libraries are quite useful in the discovery of new lead molecules in the process of drug discovery as they contain all the information related to synthesis and stability of the reaction products. In addition to this, cheminformatics tools could also search for disease pathways, target identification, and crystal structures of the target including the information of binding sites from the various databases. Cheminformatics-based compound screening methods, currently known as High-Throughput Virtual Screening (HTVS), use chemical and physical principals to identify the best candidate chemical molecule as a hit, providing a starting point to the medicinal chemist to synthesize more chemical compounds that could potentially result in a lead molecule. Additional tools like molecular dynamics simulations and docking, three-dimensional quantitative structure-activity relationship (3D-QSAR), Quantum and Molecular Mechanics (QM/MM) calculations, and free-energy calculations will further assist the medicinal chemist to synthesize the best compounds, thereby reducing the failure in the experiment as well as both time and money. Conversely, the artificial neural network is emerging as one of the most important fields in cheminformatics/current drug discovery process with considerable potential to transfer the learned knowledge from chemical databases into specific biologically active molecules. The number of already known inhibitors against various targets helps to train the test molecules regressively to find its closely related compound. Further, this process will act as a complementary method to establish the various approaches in the drug discovery process.

The special issue will focus on enabling technologies of cheminformatics, computational chemistry, or computer aided drug designing tools to enhance the role of medicinal chemistry in current trends in drug discovery.

Potential topics include but are not limited to the following:

  • High Throughput Virtual Screening (HTVS)
  • 2D/3D-QSAR, QSPR
  • QM, QM/MM
  • Fragment-based drug discovery
  • Molecular Dynamics Simulations
  • Algorithm and tools
  • Artificial neural network

Authors can submit their manuscripts through the Manuscript Tracking System at

Submission DeadlineFriday, 22 June 2018
Publication DateNovember 2018

Papers are published upon acceptance, regardless of the Special Issue publication date.

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