Current Computer - Aided Drug Design - Volume 8, Issue 3, 2012

We describe the opportunities posed by computer-assisted drug design in the light of two aspects of the current drug discovery scenario: the decline of innovation due to high attrition rates at clinical stage of development and the combinatorial explosion emerging from exponential growth of feasible small molecules and genome and proteome exploration. We present an overview of recent reports from our group in the field of rational drug development, by using topological descriptors (either alone, or in combination with different 3D approaches) and a diversity of modeling techniques such as Linear Discriminant Analysis and the Replacement Method. Modeling efforts aimed at the integrated prediction of several significant molecular properties in the field of drug discovery, such as pharmacological activity, aqueous solubility, human intestinal permeability and affinity to P-glycoprotein (ABCB1, MDR1) are reviewed. The suitability of conformation-independent descriptors to explore large chemical repositories is highlighted, as well as the opportunities posed by in silico guided drug repurposing.

Arthritis involves joint inflammation, synovial proliferation and damage of cartilage. Interleukin-1 undergoes acute and chronic inflammatory mechanisms of arthritis. Non-steroidal anti-inflammatory drugs can produce symptomatic relief but cannot act through mechanisms of arthritis. Diseases modifying anti-rheumatoid drugs reduce the symptoms of arthritis like decrease in pain and disability score, reduction of swollen joints, articular index and serum concentration of acute phage proteins. Recently, some literature references are obtained on molecular modeling of antirheumatic agents. We have tried chemometric modeling through 2D-QSAR studies on a dataset of fifty-one compounds out of which fortyfour 5-Phenylthiophenecarboxylic acid derivatives have IL-1 inhibitory activity and forty-six 5-Phenylthiophenecarboxylic acid derivatives have %AIA suppressive activity. The work was done to find out the structural requirements of these anti-rheumatic agents. 2D QSAR models were generated by 2D and 3D descriptors by using multiple linear regression and partial least square method where IL-1 antagonism was considered as the biological activity parameter. Statistically significant models were developed on the training set developed by k-means cluster analysis. Sterimol parameters, electronic interaction at atom number 9, 2D autocorrelation descriptors, information content descriptor, average connectivity index chi-3, radial distribution function, Balaban 3D index and 3D-MoRSE descriptors were found to play crucial roles to modulate IL-1 inhibitory activity. 2D autocorrelation descriptors like Broto-Moreau autocorrelation of topological structure-lag 3 weighted by atomic van der Waals volumes, Geary autocorrelation-lag 7 associated with weighted atomic Sanderson electronegativities and 3D-MoRSE descriptors like 3D-MoRSE-signal 22 related to atomic van der Waals volumes, 3D-MoRSE-signal 28 related to atomic van der Waals volumes and 3D-MoRSE-signal 9 which was unweighted, were found to play important roles to model %AIA suppressive activity.

In this review it is dealt the use of molecular topology (MT) in the selection and design of new drugs. After an introduction of the actual methods used for drug design, the basic concepts of MT are defined, including examples of calculation of topological indices, which are numerical descriptors of molecular structures. The goal is making this calculation familiar to the potential students and allowing a straightforward comprehension of the topic. Finally, the achievements obtained in this field are detailed, so that the reader can figure out the great interest of this approach.

There has been a tremendous progress in the development of anti-HIV therapies since the discovery of the HIV virus. Computer aided drug design in general and analogue-based approaches in particular have played an important role in the process of HIV drug discovery. Structure-based approaches also have played a vital role in this process. There are a large number of studies reported in the literature where QSAR methodology was employed to study the structural requirements for inhibition against various HIV targets like reverse transcriptase, protease, entry and integrase. The current review focuses on those studies and provides a detailed description on the QSAR methodology, descriptors, statistical significance and important findings. This review categorizes the reported QSAR studies on the basis of chemical scaffolds against a particular target. In reverse transcriptase category, QSAR studies on HEPT, TIBO, DABO, DAPY, DATA, AASBN, pyridone and DATZD derivatives have been reviewed. Cyclic urea, fullerene, AHPBA and dihydropyrone derivatives were considered in protease inhibitors category. In addition, QSAR studies on styrylquinoline, carboxylic acid, MBSA and chalcone derivatives were reviewed in integrase inhibitors category. QSAR studies on entry inhibitors like piperidine, benzyl piperidine, benzyl pyrazole, pyrrole and diazepane urea have also been reviewed.

Infectious diseases are a major cause of global health, economic and social problems. Relationship between the infectious diseases and drugs designed to combat them can be understood by the Quantum Pharmacology approach. Quantum pharmacology which is an amalgamation of chemistry, quantum mechanics and computer modeling aims to understand the structure activity relationship of a drug. As compared to the classical MM, the hybrid QM/MM approach which takes into account the quantum mechanics along with the molecular mechanics facilitates the simulation of biological structures with greater accuracy and speed. This review highlights the importance of quantum mechanics for a better understanding of molecular systems and QSAR studies.