Current Computer - Aided Drug Design - Volume 5, Issue 1, 2009

Non-steroidal anti-inflammatory drugs (NSAIDs) claimed during last years an increased research interest to establish their cardiovascular safety profile. Generally, NSAIDs inhibit in different degrees both isoforms of cyclooxygenase (COX). Aspirin has a unique property among NSAIDs, namely at low doses it inactivates irreversibly the COX-1 activity in platelets. It is well known that platelets are a significant source of inflammatory mediators and their activation leads to important clinical atherothrombotic vascular events. Atherosclerosis is a chronic inflammatory process. The cardioprotective effect of aspirin resides in it's mechanism of action, suppressing the platelet COX-1 dependent thromboxane biosynthesis. There are patients who do not benefit from the cardioprotective effect of aspirin, being labeled as “resistant” to aspirin. The underlying mechanism of aspirin resistance is yet unclear. This review intends to detail recent advances in the field of molecular simulation applied to nonselective non-aspirin NSAIDs and other COX selective inhibitors. Binding studies were performed between the COX-2 enzyme and these molecules. Using 2D-QSAR, it was noticed that the lipophilic bulkier group width-wise is required for a significant biological activity and also, the hydrophobic interactions might be crucial for the potency of same COX inhibitors. In order to understand a meaningful comparison of both classical NSAIDs and newer COX-2 inhibitors, three-dimensional quantitative structure-activity relationships and also molecular docking techniques were applied.

In 2006, 80% of the small molecule drugs approved by Food and Drug Administration (FDA) of USA were chiral and 75% were single enantiomers. It is expected that 200 chiral compounds could enter the development process every year. In order to keep pace with the industry, computational chemists are trying to develop chirality measures to assist and direct asymmetric synthesis and chiral catalysis. Parameterization of chirality and development of chirality metrics, are very important in QSAR approach to be applied to chiral molecules. There are several attempts in the development of chirality measurements and earlier reviews on chirality measures concentrated more on the mathematics involved in their calculations. This review presents in-depth discussions of various chirality measures from the perspective of a QSAR modeler.

The progress in chemical knowledge and synthetic technologies over the last fifty-years has dramatically increased the synthetic accessible chemical entities. Exploration of natural products rich chemodiversity has also expanded the vast chemical universe where medicinal chemist can pursue the identification of new therapeutic agents. Virtual Screening (VS) benefits from computational technology to explore the increasingly vast chemical universe in an efficient manner. The different VS approaches may be characterized by the computational and human time they require, from the highly automated and fast 2D-QSAR ligand-based VS to the more demanding 3D QSAR and target-based (docking) methodologies. Recently, several studies based on the integration of different VS approaches have been proposed, demonstrating that the hit recovery rate may be maintained (or even increased) with a substantial reduction of computing times. Combined virtual screening methodologies usually begin with the least-demanding approaches at the beginning of the VS process and progress to the more accurate, time consuming techniques in the last stages. This review discusses recent 2D/3D QSAR and ligand-based/target-based “synergistic” combinations that allow speeding-up the VS process, permitting accurate and efficient studies on large databases. The impact of the combination of different techniques on the chemical diversity of the compounds retrieved is also discussed.

Human immunodeficiency virus type 1 protease (HIV-1 PR) is an essential enzyme for the replication cycle of HIV-1. HIV-1 PR inhibitors have been extensively investigated as anti-AIDS drugs. For developments of HIV-1 PR inhibitors more promising than those utilized at the moment, the construction of reliable QSAR models that can elucidate the inhibitory mechanism as consistently as possible should be one of the most significant issues. Garg, Kurup, and their groups published comprehensive QSAR studies using past structure-activity data for HIV-1 PR inhibitors, and summarized some physicochemical structural factors of inhibitors that govern variations in the inhibitory activity for various structural types. There seem to exist much to be clarified further, especially for effects of electronic structure of inhibitors. It is also expected to incorporate structural and physicochemical information about the enzyme protein into the QSAR model. In this article, we reviewed our own QSAR study on a series of cyclic urea inhibitors with newly proposed QSAR descriptors. We performed molecular dynamics simulations of HIV-1 PR-inhibitor complexes to provide the accurate geometry to the fragment molecular orbital (FMO) calculations as well as to the estimation of an accessible surface area descriptor for inhibitors and amino acid residues. With the FMO procedure to cover full electronic feature of three-dimensional structure of protease-inhibitor complexes, we derived electronic descriptors for inhibitors and amino acid residues. The successful results are believed to provide a new insight into QSAR and understanding of binding mechanism of inhibitors with HIV-1 PR at atomic and electronic levels.

Computer-aided drug design (CADD) methodologies have proven to be very effective, greatly enhancing the efficiency of small molecule drug discovery and development processes. These methods include quantitative structureactivity relationship and pharmacophore models, quantitative structure-property relationship models, as well as in silico docking studies. While docking studies very often correctly identify the binding mode of a ligand, they have reduced success in predicting binding affinities. Development of improved and more efficient strategies for scoring binding affinity is a very active area of research. Here we review the utility of computational intelligence approaches such as artificial neural networks, fuzzy logic, and evolutionary computation to the calculation of improved docking scores.