Current Topics in Medicinal Chemistry

ISSN: 1568-0266

Current Topics in Medicinal Chemistry
Volume 6, Number 1, 2006

Contents

Computational Approaches in Medicinal Chemistry: Surveys, Case Studies and Future Directions
Guest Editor: Gerald H. Lushington

Editorial Pp. 1-2


Diversity in Medicinal Chemistry Space Pp. 3-17
Alain-Dominique Gorse
[Abstract]


Recent Developments in Focused Library Design: Targeting Gene-Families Pp. 19-29
Jennifer L. Miller
[Abstract]


Decision Tree Methods in Pharmaceutical Research Pp. 31-39
Paul E. Blower and Kevin P. Cross
[Abstract]


Computational Approaches to Model Ligand Selectivity in Drug Design Pp. 41-55
Angel R. Ortiz, Paulino Gomez-Puertas, Alejandra Leo Macias,Pedro LopezRomero, Eduardo Lopez-Viñas, Antonio Morreale and Marta Murcia, Kun Wang
[Abstract]


Acetylcholinesterase: Molecular Modeling with the Whole Toolkit Pp. 57-73
Gerald H. Lushington, Jian-Xin Guo and Margaret M. Hurley
[Abstract]




Abstracts

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Editorial

Computational Approaches in Medicinal Chemistry: Surveys, Case Studies and Future Directions

As the twin pressures of efficiency and economy continue to mount, the role of computers in pharmaceutical research has evolved from humble complement to vital component. Once largely a tool for explaining empirical observations, computer modeling is now a critical part of hypothesis formulation, and provides a rigorous testing environment for validation at all design stages from initial synthesis to clinical trial and therapeutic implementation. Increasingly the success of drug design and development is becoming tied to continued reformulation and optimization of computational techniques. These development and optimization issues are a common thread throughout this edition of Current Topics in Medicinal Chemistry. While the breadth of available in silico techniques is too great to survey in one issue, this edition should help to survey key areas in the field and highlight a subset of emerging concepts, techniques and strategies for pursuing research across a number of the most important sub-fields of computational medicinal chemistry. Thus, a series of five papers have been assembled covering seminal aspects of chemical diversity analysis, chemical library targeting, structure-activity relationship determination, pharmacophore perception, and structure-based design.

One area wherein pharmaceutical research has been undergoing dramatic upheaval is chemical library formulation. Attempts to uncover the next insulin or prozac via blind chemical combinatorics have proven resoundingly unsuccessful and mainly serve to demonstrate that the vast majority of Lipinski-compliant chemical space is therapeutically barren. Large diverse chemical libraries remain useful in that biochemical targets and pathways remain far too complex and varied for even the most seasoned researcher to fully intuit. However, while there will undoubtedly continue to be surprising chemical discoveries, the need to improve efficiency and hit prospects by incorporating biological knowledge into the library design process has emerged. In this light, Dominique Gorse has provided a thorough survey of diversity analysis techniques, with an emphasis on emerging schemes that balance diversity with a propensity for bioactivity and druggability, while Jennifer Miller introduces and elaborates on new paradigms for tailoring compound libraries toward inhibition of specific gene families.

Using high throughput screening to extract large chemical libraries for pharmacological knowledge has proven time-consuming and expensive, thus data-mining methods are being actively aimed at library filtering, so as to extract potent subsets on which to focus assays. The challenge of developing predictive hit / inactive classification schemes based on non-ideal scenarios (e.g., small amounts of prior data, inadequate chemical diversity, data inhomogeneity, etc.) has spurred extensive computational algorithmic exploration. Paul Blower and Kevin Cross have summarized the resulting techniques in detail, emphasizing decision tree methods that exhibit special promise in successful exploitation of a maximum quantity of available data by virtue of an intrinsic ability to incorporate inhomogeneous data of non-uniform quality.

The emerging capacity for deriving consensus pharmacophore models from assay data and functional genomic information is revolutionizing the ways in which computational approaches contribute to drug design, permitting the assimilation of highly complex multivariate information into an intuitively instructive model. Model construction is not simple, however, in that a successful scheme must be able to resolve varying ligand specificities of a myriad of similar drug targets. Angel Ortiz and his colleagues have systematically explored the key interdependent techniques being applied to this goal. From sequence-based bioinformatics, they describe how functional relationships between different targets are ascertained, then introduce protein structure information in order construct binding site postulates, and finally evaluate the importance of specific pharmacophoric interactions via correlations to activity data, placing special emphasis on the COMBINE (COMparative BINding Energy) method for the latter analysis.

The ultimate goal of such models is a translation to new therapeutic or prophylactic paradigms. Models of rigid, well-characterized receptors often readily yield reliable predictions, but many other targets suffer from complications such as receptor flexibility, variable site accessibility, and covalent binding effects. One system posing all such impediments is Acetylcholinesterase: a serine hydrolase vital to neural function, a known target for Myasthenia Gravis, Alzheimer's Disease and Parkinson's Disease treatment, and infamously susceptible to organophosphorus neurotoxins. A flexible gorge variably restricts ligand access, ligand response effects render conventional docking simulations ineffectual, and covalent binding effects, intractable to classical dynamics simulations, are prevalent. In the final paper Lushington et al. explore how such methodological deficiencies may be addressed via a multi-pronged modeling strategy incorporating 3D QSAR, tailored molecular docking, dynamics, and quantum mechanics, with substantial positive feedback between the methods. By examining the failures and successes encountered on one such challenging target we hope to offer a template for probing other dynamic targets of current or future interest to the pharmaceutical industry.

Dr. Gerald H. Lushington
Molecular Graphics & Modeling Laboratory
University of Kansas
Lawrence, KS, 66045
USA


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Diversity in Medicinal Chemistry Space
Alain-Dominique Gorse

The chemical universe containing organic molecules within a reasonable molecular weight is vast and largely unexplored. Estimations of possible numbers of unique molecules range from 10¹³ to 10¹⁸⁰. These numbers have to be compared with the few tens of millions of compounds currently known. Design of libraries that populate the medicinally relevant chemical subspace and tools that help to maximise the chance of identifying leads are necessary. This review describes various molecular representations that lead to the definition of chemical space, drug space or activity space. Strategies for compound selection in such spaces are discussed, as well as potential sources of diversity that could be used to explore the medicinal space in quest of new drugs.


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Recent Developments in Focused Library Design: Targeting Gene-Families
Jennifer L. Miller

For many years, the most frequently optimized qualities of a screening library, or corporate compound collection, were size and diversity. Maximizing the number of diverse hits is the fundamental goal of such strategies. The ostensible justification that “bigger is better” is based on the large, estimated size of small-molecule space and the hypothesis that the notoriously low hit rates from high-throughput screening (HTS) could be overcome by brute force: i.e. by screening more compounds. Published, detailed studies about the success (or failure) of the brute-force strategy are rare, but it is well-known that it did not fulfill expectations. As a result, published reports in recent years have increasingly described methods for designing, selecting or synthesizing gene family-focused or -biased libraries. Moreover, many of the larger compound suppliers now sell such libraries, reflecting the growing interest in them from both the pharmaceutical and biotechnology markets. The trend towards gene family-focused libraries marks the emergence of a different hypothesis about how to increase HTS hit rates and also reflects an increasingly pragmatic focus on the management of screening libraries. An important, underlying assumption in this trend is that a high-quality, general-purpose screening library of manageable size is neither realizable nor desirable. Whether a biasing strategy based on a specific gene family will do a better job of meeting both the scientific and business needs of the drug discovery enterprise still remains to be seen, but it is certainly an active area of current research. This review focuses on the “who, what, why, when, and how” of the design of gene family-focused libraries. Particular attention is given to reports that discuss not only the techniques used, but also any results obtained.


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Decision Tree Methods in Pharmaceutical Research
Paul E. Blower and Kevin P. Cross

Decision trees are among the most popular of the new statistical learning methods being used in the pharmaceutical industry for predicting quantitative structure-activity relationships. This article reviews applications of decision trees in drug discovery research and extensions to the basic algorithm using hybrid or ensemble methods that improve prediction accuracy.


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Computational Approaches to Model Ligand Selectivity in Drug Design
Angel R. Ortiz, Paulino Gomez-Puertas, Alejandra Leo Macias,Pedro LopezRomero, Eduardo Lopez-Viñas, Antonio Morreale and Marta Murcia, Kun Wang

To be effective, a designed drug must discriminate successfully the macromolecular target from alternative structures present in the organism. The last few years have witnessed the emergence of different computational tools aimed to the understanding and modeling of this process at molecular level. Although still rudimentary, these methods are shaping a coherent approach to help in the design of molecules with high affinity and specificity, both in lead discovery and in lead optimization. It is the purpose of this review to illustrate the array of computational tools available to consider selectivity in the design process, to summarize the most relevant applications, and to sketch the challenges ahead.


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Acetylcholinesterase: Molecular Modeling with the Whole Toolkit
Gerald H. Lushington, Jian-Xin Guo and Margaret M. Hurley

Molecular modeling efforts aimed at probing the structure, function and inhibition of the acetylcholinesterase enzyme have abounded in the last decade, largely because of the system's importance to medical conditions such as myasthenia gravis, Alzheimer's disease and Parkinson's disease, and well as its famous toxicological susceptibility to nerve agents. The complexity inherent in such a system with multiple complementary binding sites, critical dynamic effects and intricate mechanisms for enzymatic function and covalent inhibition, has led to an impressively diverse selection of simulation techniques being applied to the system, including quantum chemical mechanistic studies, molecular docking prediction of noncovalent complexes and their associated binding free energies, molecular dynamics conformational analysis and transport kinetics prediction, and quantitative structure activity relationship modeling to tie salient details together into a coherent predictive tool. Effective drug and prophylaxis design strategies for a complex target like this requires some understanding and appreciation for all of the above methods, thus it makes an excellent case study for multi-tiered pharmaceutical modeling. This paper reviews a sample of the more important studies on acetylcholinesterase and helps to elucidate their interdependencies. Potential future directions are introduced based on the special methodological needs of the acetylcholinesterase system and on emerging trends in molecular modeling.