Current Topics in Medicinal Chemistry - Volume 10, Issue 1, 2010

The quote from Charles Dickens's novel is most appropriate in the drug discovery and development field. Concepts are constantly evolving in response to changing economic opportunities and technological advancements. Traditional medicinal chemistry paradigms, relying initially on ‘wet’ chemistry followed by screening and lead optimizations, are expensive and time consuming. On the other hand, initial in silico screening that guides the synthesis and screening of selected compounds has proven to be a better approach to accelerate drug discovery and reduce the cost of the discovery phase. The past 10 years have seen unprecedented scientific advances, with sequencing of the various genomes coupled with advances in proteomics, bioinformatics and cheminformatics, all of which have provided a wealth of information to help speed up drug discovery research and bring newer and better therapies to the market place. The aim of this special issue is to give an overview of and highlight the latest achievements in various computational approaches at a point in time when the field is experiencing tremendous algorithmic advancements in terms of speed and accuracy, with a constant enthusiasm and excitement to meet up the experiments. In this context, virtual screening of small-molecule libraries has attracted academic and industry researchers' attention as a central lead identification and scaffold hopping tool. Amaro et al. elaborate recent advances and future prospects of methods in virtual screening, specifically highlighting issues with protein flexibility and more rigorous estimates of free energies with an aim to rank order the hits and increase enrichment. Loving et al. review various fragment-based discovery and de novo design protocols with cases of successful applications in real-world drug discovery projects. They also exemplify the strengths and weaknesses of these approaches and discuss how one method can be used to complement another and the experiment. The fundamentals of molecular interactions are well understood, but there are challenges to implementing good physical models for hundreds of thousands of possible ligands. Indeed, an accurate calculation of absolute binding affinity in screens of large, diverse libraries is beyond the scope of present tools, but the rank ordering of affinities of disparate hits should be a working compromise. Consequently, Zhau et al. and Menikarachchi et al. discuss the roles of QM and hybrid QM/MM methods, respectively and emphasize their applications to medicinal chemistry problems. Water plays multiple roles in the life of organisms, as a moderator and mediator in protein-ligand interactions. De Beer et al. discuss the influence of water on the ligand-receptor binding process and evaluate the methods of modeling this influence in computational drug design. Further, they review the methods to predict the presence of displaceable waters in protein-ligand complexes, and critically discuss methods of including water in computational drug research. Hydrophobicity impacts every aspect of drug design and even delivery, and determines one of the Lipinski's rule-of-five parameters. Sarkar et al. give a thought-provoking historical perspective on hydrophobicity research, HINT forcefield and its applications in the protein folding and drug design. The Protein Data Bank (PDB) is growing at an ever-increasing rate, but bridging the exponentially increasing gap between the number of protein sequences and their experimental 3D structures will remain beyond its reach. Protein modeling methods try to bridge this gap and have proved efficient in the past in drug design projects. Daga et al. bring together different approaches currently being utilized for 3D structure generation with their advantages (accuracy versus speed), limitations and pitfalls. With its existence over half a century, QSAR still remains one of the prominent computational techniques in structureactivity analysis and screening, thereby expanding its horizons to both ligand-based and structure-based scenarios. QSAR has been the single most-used computational technique and is growing further with newer algorithms and strategies. Verma et al. assess various (nD) QSAR techniques and critically argue the problems associated with alignment-dependency, conformational sensitivity, and choice of statistical methods, biological readout and validation strategies. The pharmaceutical properties of drug candidates determine how much of the drug safely reaches the therapeutic target and dictate its future success as a drug. This so called ADME/T profiling helps guide researchers to optimize the pharmacokinetic and pharmacodynamic properities of lead candidates under development. Prashant Kharkar throws light on newer ADMET prediction algorithms and recent developments to accurately predict these properties. He also traces the underlying obstacles in developing models with better predictivity, with local and/or global applicability and possibility of replacing the in vitro ADME/T testing with in silico screens in the not so distant future. Lastly, Talele et al. make an attempt to highlight the performance of computer-aided drug design field over the years by evaluating the role of computational modeling in identifying and/or optimizing the lead candidates that have entered or successfully outshined the clinical phases of evaluation. Finally, I would like to take this opportunity to thank all the contributing authors of this issue for their valuable time and effort to help this compilation. My special thanks to all the experts who evaluated the article to improve the quality and readability in a timely manner. I extend my special appreciation to Dr. Allen Reitz, editor-in-chief of Current Topics in Medicinal Chemistry, for providing me with this exciting opportunity, and to the editorial and publication team of Bentham Science for their cooperation during the entire process of bringing this issue to the readers. I also thank readers and welcome feedback that will eventually help improve our current state of knowledge and understanding of the field.

Ensemble based virtual screening refers to the use of conformational ensembles from crystal structures, NMR studies or molecular dynamics simulations. It has gained greater acceptance as advances in the theoretical framework, computational algorithms, and software packages enable simulations at longer time scales. Here we focus on the use of computationally generated conformational ensembles and emerging methods that use these ensembles for discovery, such as the Relaxed Complex Scheme or Dynamic Pharmacophore Model. We also discuss the more rigorous physics-based computational techniques such as accelerated molecular dynamics and thermodynamic integration and their applications in improving conformational sampling or the ranking of virtual screening hits. Finally, technological advances that will help make virtual screening tools more accessible to a wider audience in computer aided drug design are discussed.

Fragment-based and de novo design strategies have been used in drug discovery for years. The methodologies for these strategies are typically discussed separately, yet the applications of these techniques overlap substantially. We present a review of various fragment-based discovery and de novo design protocols with an emphasis on successful applications in real-world drug discovery projects. Furthermore, we illustrate the strengths and weaknesses of the various approaches and discuss how one method can be used to complement another. We also discuss how the incorporation of experimental data as constraints in computational models can produce novel compounds that occupy unique areas in intellectual property (IP) space yet are biased toward the desired chemical property space. Finally, we present recent research results suggesting that computational tools applied to fragment-based discovery and de novo design can have a greater impact on the discovery process when coupled with the right experiments.

Quantum mechanical (QM) methods are becoming popular in computational drug design and development mainly because high accuracy is required to estimate (relative) binding affinities. For low- to medium-throughput in silico screening, (e.g., scoring and prioritizing a series of inhibitors sharing the same molecular scaffold) efficient approximations have been developed in the past decade, like linear scaling QM in which the computation time scales almost linearly with the number of basis functions. Furthermore, QM-based procedures have been used recently for determining protonation states of ionizable groups, evaluating energies, and optimizing molecular structures. For highthroughput in silico screening QM approaches have been employed to derive robust quantitative structure-activity relationship models. It is expected that the use of QM methods will keep growing in all phases of computer-aided drug design and development. However, extensive sampling of conformational space and treatment of solution of macromolecules are still limiting factors for the broad application of QM in drug design.

One of the goals of medicinal chemistry concerns the ability to compute protein-ligand interactions based on the structural knowledge of the receptor. To this end, the majority of current approaches incorporate classical force field potentials to describe receptor-ligand interactions. One of the most critical problems of standard molecular mechanics (MM) force fields is their fixed-charge treatment of electrostatic interactions. Two problems are derived from this approximation, polarization and charge transfer. As an immediate step in computational complexity, it seems natural to incorporate Quantum Mechanics (QM) within a hybrid QM/MM approach, which has shown to be a useful tool to describe structural and mechanistic aspects of chromophores and prosthetic residues in proteins. In this review, we describe specifically the role of QM/MM methods and their various applications to computational drug design and medicinal chemistry research in general.

Although water molecules are small and only consist of two different atom types, they play various roles in cellular systems. This review discusses their influence on the binding process between biomacromolecular targets and small molecule ligands and how this influence can be modeled in computational drug design approaches. Both the structure and the thermodynamics of active site waters will be discussed as these influence the binding process significantly. Structurally conserved waters cannot always be determined experimentally and if observed, it is not clear if they will be replaced upon ligand binding, even if sufficient space is available. Methods to predict the presence of water in protein-ligand complexes will be reviewed. Subsequently, we will discuss methods to include water in computational drug research. Either as an additional factor in automated docking experiments, or explicitly in detailed molecular dynamics simulations, the effect of water on the quality of the simulations is significant, but not easily predicted. The most detailed calculations involve estimates of the free energy contribution of water molecules to protein-ligand complexes. These calculations are computationally demanding, but give insight in the versatility and importance of water in ligand binding.

Hydrophobic interactions are some of the most important interactions in nature. They are the primary driving force in a number of phenomena. This is mostly an entropic effect and can account for a number of biophysical events such as protein-protein or protein-ligand binding that are of immense importance in drug design. The earliest studies on this phenomenon can be dated back to the end of the 19th century when Meyer and Overton independently correlated the hydrophobic nature of gases to their anesthetic potency. Since then, significant progress has been made in this realm of science. This review briefly traces the history of hydrophobicity research along with the theoretical estimation of partition coefficients. Finally, the application of hydrophobicity estimation methods in the field of drug design and protein folding is discussed.

Protein modeling has been a very challenging problem in drug discovery and computational biology. The latest advances and progress in computational power have helped to solve this problem to a considerable extent; however, predicting accurate three-dimensional structure of proteins has always been and remains a complicated assignment. Of the two common methods of protein structure prediction, template-based modeling has become more popular than ab initio modeling. In this review, we summarize the developments in methodology and of understanding for comparative protein modeling during the last three years, including for homologue search, fold recognition, secondary structure prediction, model building, loop building, side-chain prediction and model quality assessment.

Quantitative structure-activity relationships (QSAR) have been applied for decades in the development of relationships between physicochemical properties of chemical substances and their biological activities to obtain a reliable statistical model for prediction of the activities of new chemical entities. The fundamental principle underlying the formalism is that the difference in structural properties is responsible for the variations in biological activities of the compounds. In the classical QSAR studies, affinities of ligands to their binding sites, inhibition constants, rate constants, and other biological end points, with atomic, group or molecular properties such as lipophilicity, polarizability, electronic and steric properties (Hansch analysis) or with certain structural features (Free-Wilson analysis) have been correlated. However such an approach has only a limited utility for designing a new molecule due to the lack of consideration of the 3D structure of the molecules. 3D-QSAR has emerged as a natural extension to the classical Hansch and Free-Wilson approaches, which exploits the three-dimensional properties of the ligands to predict their biological activities using robust chemometric techniques such as PLS, G/PLS, ANN etc. It has served as a valuable predictive tool in the design of pharmaceuticals and agrochemicals. Although the trial and error factor involved in the development of a new drug cannot be ignored completely, QSAR certainly decreases the number of compounds to be synthesized by facilitating the selection of the most promising candidates. Several success stories of QSAR have attracted the medicinal chemists to investigate the relationships of structural properties with biological activity. This review seeks to provide a bird's eye view of the different 3D-QSAR approaches employed within the current drug discovery community to construct predictive structure- activity relationships and also discusses the limitations that are fundamental to these approaches, as well as those that might be overcome with the improved strategies. The components involved in building a useful 3D-QSAR model are discussed, including the validation techniques available for this purpose.

With the dawn of new century, major technological advances in the drug discovery field have revolutionized absorption, distribution, metabolism, excretion and toxicity (ADME/T) profiling of new chemical entities (NCEs) among others. The progress made in the in vitro experimental determination of the ADME/T properties fueled the growth in the so-called predictive ADME/T. The process of in silico model development improved significantly with the availability of high quality data as well newer, more accurate statistical methods of analysis. Even several such models appear in the literature regularly, the prediction accuracy is limited to ‘local’ rather than ‘global’ applicability domain in majority of the cases. Majority of the efforts are still needed to address several issues such as data quality, accuracy of the results, etc., to increase the usefulness of these models. The ultimate aim is to develop in silico ADME/T models which will largely replace their in vitro experimental counterparts. The current review article discusses the two-dimensional (2D) approaches used in the predictive ADME/T model development and their limitations and usefulness in the discovery process.

Drug discovery and development is an interdisciplinary, expensive and time-consuming process. Scientific advancements during the past two decades have changed the way pharmaceutical research generate novel bioactive molecules. Advances in computational techniques and in parallel hardware support have enabled in silico methods, and in particular structure-based drug design method, to speed up new target selection through the identification of hits to the optimization of lead compounds in the drug discovery process. This review is focused on the clinical status of experimental drugs that were discovered and/or optimized using computer-aided drug design. We have provided a historical account detailing the development of 12 small molecules (Captopril, Dorzolamide, Saquinavir, Zanamivir, Oseltamivir, Aliskiren, Boceprevir, Nolatrexed, TMI-005, LY-517717, Rupintrivir and NVP-AUY922) that are in clinical trial or have become approved for therapeutic use.