Combinatorial Chemistry & High Throughput Screening - Volume 14, Issue 6, 2011

In the early days of combinatorial chemistry, molecular diversity was the most important property to be considered for its application in drug discovery. Over the years, it was also realized that enrichment of chemical libraries with molecules of interest for particular targets could be another way to evolve compounds into realistic drug candidates. Thus, interest in library design has shifted towards the generation of the so-called target-class focused libraries. To address this question, several computational methods have emerged in order to make library design a cost-effective approach. Ligand and target-based in silico approaches have become common practice in the drug-discovery process. In addition to the search for target candidates, computational approaches are also playing a crucial role in predicting pharmacokinetics features of chemicals in the early stages of lead discovery. In this context, a myriad of computational models has also become an integrated part of the library design, especially to filter and discard compounds that are unlikely to become drugs prior to synthesis or commercial acquisition. In this special issue of Combinatorial Chemistry and High Throughput Screening, the authors provide an overview of selected advances in computational algorithms for the rational preparation of compound libraries. All of these techniques are reviewed and presented by authors coming both from the industry and academia, thus showing the complementary efforts and contributions from both sides. Very different strategies are presented, from ligand-based approaches (such as the generation of global or specific-target QSAR models, or the development of pharmacophore hypotheses) to target-based techniques (comparative modeling, docking and scoring approaches, virtual screening). Management of databases and fragment-based strategies are also discussed. Particular emphasis on real cases of application of all of these in silico approaches is provided, highlighting several success stories and thus demonstrating the very important role that chemoinformatics and molecular modeling techniques play in the generation of focused libraries and drug discovery.

Due to the huge amount of data generated in drug discovery programs, their success strongly depends on both the workflows and platforms to manage and, more importantly, to integrate different chemical and biological data sources. At Experimental Therapeutics Program in the Spanish National Cancer Research Center (CNIO), we have addressed our efforts in the design and optimal implementation of those key processes that enable dynamic workflows and interfaces between the different information blocks. Our approach focuses on the development of a common chemical and biological repository (CCBR) that gathers all data that pass quality control criteria. An integral web application (WACBIP) was designed to query against CCBR while providing decision making tools. Currently, our CCBR contains more than 43,000 unique structures as well as experimental data from more than 350 different biological assays. As input sources of the CCBR, we federated a series of Laboratory Information Management Systems (LIMS) which cover sections as follows: chemical synthesis, analytical department, compound logistics, biochemical and cellular data (including high throughput and high-content screenings; HTS and HCS), computational chemistry (in silico chemogenomics and physico-chemical profiling) and in vivo pharmacology. With regard to the last section, an integral In vivo Management e-Biobook (IVMB) that handles the entire workflow of in vivo labs was designed and implemented. Herein we describe the processes and tools that we have developed and implemented, balancing purchase and development, for centralizing discovery information as well as providing decision-making and project management tools - a clear unmet need in public organizations and networks.

Quantitative structure-activity relationships (QSARs) have important applications in drug discovery research, environmental fate modeling, property prediction, etc. Validation has been recognized as a very important step for QSAR model development. As one of the important objectives of QSAR modeling is to predict activity/property/toxicity of new chemicals falling within the domain of applicability of the developed models and QSARs are being used for regulatory decisions, checking reliability of the models and confidence of their predictions is a very important aspect, which can be judged during the validation process. One prime application of a statistically significant QSAR model is virtual screening for molecules with improved potency based on the pharmacophoric features and the descriptors appearing in the QSAR model. Validated QSAR models may also be utilized for design of focused libraries which may be subsequently screened for the selection of hits. The present review focuses on various metrics used for validation of predictive QSAR models together with an overview of the application of QSAR models in the fields of virtual screening and focused library design for diverse series of compounds with citation of some recent examples.

Virtual screening is increasingly being used in drug discovery programs with a growing number of successful applications. Experimental methodologies developed to speed up the drug discovery processes include high throughput screening and combinatorial chemistry. The complementarities between computational and experimental screenings have been recognized and reviewed in the literature. Computational methods have also been used in the combinatorial chemistry field, in particular in library design. However, the integration of computational and combinatorial chemistry screenings has been attempted only recently. Combinatorial libraries (experimental or virtual) represent a notable source of chemically related compounds. Advances in combinatorial chemistry and deconvolution strategies, have enabled the rapid exploration of novel and dense regions in the chemical space. The present review is focused on the integration of virtual and experimental screening of combinatorial libraries. Applications of virtual screening to discover novel anticancer agents and our ongoing efforts towards the integration of virtual screening and combinatorial chemistry are also discussed.

A pharmacophore is a model which represents the key physico-chemical interactions that mediate biological activity. There is a long history of using pharmacophore modeling methods to select subsets of compounds, focused towards a specific target of interest. This paper will review existing computational methods for deriving and comparing pharmacophore models. We outline a new classification of pharmacophore methods based on the abstraction of the underlying chemical interactions which embody a pharmacophore, and the methods available to quantitatively compare them. Within the context of this classification, example studies, using specific pharmacophore modeling methods for focused library selection, will be discussed.

Fragment-based screening is an emerging technology which is used as an alternative to high-throughput screening (HTS), and often in parallel. Fragment screening focuses on very small compounds. Because of their small size and simplicity, fragments exhibit a low to medium binding affinity (mM to μM) and must therefore be screened at high concentration in order to detect binding events. Since some issues are associated with high-concentration screening in biochemical assays, biophysical methods are generally employed in fragment screening campaigns. Moreover, these techniques are very sensitive and some of them can give precise information about the binding mode of fragments, which facilitates the mandatory hit-to-lead optimization. One of the main advantages of fragment-based screening is that fragment hits generally exhibit a strong binding with respect to their size, and their subsequent optimization should lead to compounds with better pharmacokinetic properties compared to molecules evolved from HTS hits. In other words, fragments are interesting starting points for drug discovery projects. Besides, the chemical space of low-complexity compounds is very limited in comparison to that of drug-like molecules, and thus easier to explore with a screening library of limited size. Furthermore, the “combinatorial explosion” effect ensures that the resulting combinations of interlinked binding fragments may cover a significant part of “drug-like” chemical space. In parallel to experimental screening, virtual screening techniques, dedicated to fragments or wider compounds, are gaining momentum in order to further reduce the number of compounds to test. This article is a review of the latest news in both experimental and in silico virtual screening in the fragment-based discovery field. Given the specificity of this journal, special attention will be given to fragment library design.

Target-focused compound libraries are collections of compounds which are designed to interact with an individual protein target or, frequently, a family of related targets (such as kinases, voltage-gated ion channels, serine/cysteine proteases). They are used for screening against therapeutic targets in order to find hit compounds that might be further developed into drugs. The design of such libraries generally utilizes structural information about the target or family of interest. In the absence of such structural information, a chemogenomic model that incorporates sequence and mutagenesis data to predict the properties of the binding site can be employed. A third option, usually pursued when no structural data are available, utilizes knowledge of the ligands of the target from which focused libraries can be developed via scaffold hopping. Consequently, the methods used for the design of target-focused libraries vary according to the quantity and quality of structural or ligand data that is available for each target family. This article describes examples of each of these design approaches and illustrates them with case studies, which highlight some of the issues and successes observed when screening target-focused libraries.

The goal of computational protein structure prediction is to provide three-dimensional (3D) structures with resolution comparable to experimental results. Comparative modeling, which predicts the 3D structure of a protein based on its sequence similarity to homologous structures, is the most accurate computational method for structure prediction. In the last two decades, significant progress has been made on comparative modeling methods. Using the large number of protein structures deposited in the Protein Data Bank (∼65,000), automatic prediction pipelines are generating a tremendous number of models (∼1.9 million) for sequences whose structures have not been experimentally determined. Accurate models are suitable for a wide range of applications, such as prediction of protein binding sites, prediction of the effect of protein mutations, and structure-guided virtual screening. In particular, comparative modeling has enabled structure-based drug design against protein targets with unknown structures. In this review, we describe the theoretical basis of comparative modeling, the available automatic methods and databases, and the algorithms to evaluate the accuracy of predicted structures. Finally, we discuss relevant applications in the prediction of important drug target proteins, focusing on the G protein-coupled receptor (GPCR) and protein kinase families.

Chemoinformatics is a scientific discipline at the interface between chemistry and computer science, which nowadays is currently implemented in pharmaceutical companies as a part of the usual drug discovery pathway. Furthermore, taking into account the vast amount of experimental and computational data currently generated on drug discovery projects, the use of chemoinformatics tools has become increasingly necessary. Most of chemoinformatics projects are initiated from information stored in large databases of chemicals. The compounds are systematically characterized by numerical descriptors in order to manage this information and obtain some kind of chemical-biological relationships by similarity/diversity analysis, QSAR development for ADMET predictions, chemical space navigation for the selection of drug subspaces, drug-like and lead-like selection, or substructure searching. In this paper, we will review some of the more important chemical databases of small molecules and the descriptors that can be used to describe them, as well as their applications to the specific area of design of focused/targeted libraries.

Rafael Gozalbes holds a Ph.D. in Physical Chemistry from the University of Valencia, Spain (1998). After a post-doctoral position at the “Groupe de Chimie Informatique et Modelisation” (ITODYS) and Faculte de Medecine, both at the Universite Paris VII, he spent seven years as senior scientist at the Modelling group of CEREP, a biotech company, in France. Since 2007 he is scientific collaborator at the Structural Biochemistry Laboratory of the Centro de Investigacion Principe Felipe (CIPF). His current laboratory is actively engaged in the structural exploration of protein targets relevant in cell invasion and metastasis by using NMR spectroscopy, as well as fragment-based approaches for hit-identification. In this context, Dr. Gozalbes has the responsibility of the chemoinformatics and molecular modelling activities related to the group projects, and he has participated in several programs financed by public institutions as well as translational projects supported by pharmaceutical companies. Dr. Gozalbes is a computational chemist with expertise in the application of in silico approaches to drug discovery, and in particular the development of QSAR multivariate models (for physico-chemical, ADME-T and biological predictions), docking and study of protein-ligand interactions, design of virtual target-focused and diverse chemical libraries, pharmacophore hypotheses generation and virtual screening for selection of hit candidates. He has reviewed research programs for international institutions such as the Institut Pasteur - Cenci Bolognetti Foundation of Rome University “La Sapienza” (Italy) or the Fonds de recherche sur la nature et les technologies (FQRNT), Quebec, Canada. He has published more than 20 scientific papers and two book chapters, and collaborates regularly as a scientific reviewer of several computational and medicinal chemistry journals.