Gerhard Klebe

In the next couple of years the human genome will be fully sequenced [1]. This will provide us with the sequence and overall function of all human genes as well as the complete genome formany microorganisms. Subsequently it is hoped, that by means of powerful bioinformatic tools, the gene variants can be determined that contribute to various multifactorial diseases and genes that exist in certain infectious agents but not humans. As a consequence, this will allow us to define the most appropriate levels for drug intervention. It can be expected that the number of potential drug targets will increase, possibly by a factor of 10 or more [2,3]. Nevertheless, sequencing the human genome or, for that matter, the genome of other species will be only the starting point for the understanding of their biological function. Structural genomics is a likely follow-up, combined with new techniques to validate the therapeutic relevance of such newly discovered targets [4]. Accordingly, it can be expected that in the near future we will witness a substantial increase in novel putative targets for drugs. To address these new targets effectively, we require new approaches and innovative tools [3].

At present two alternative, however complementary, techniques are employed: experimental high-throughput screening (HTS) of large compound libraries, increasingly provided by combinatorial chemistry, and computational methods for virtual screening (VS) and de novo design [5]. Experimental HTS involves highly sophisticated robotics and advanced engineering know-how. Appropriate molecular test systems have to be automated and adapted to the conditions of HTS. Advanced computer and informatics technology has to handle the logistics and the immense data flow. HTS typically produces a tremendous amount of ligand binding data with typical hit rates of about 1%. Perhaps, at first glance, this figure appears quite low. However, considering one to several million compounds to be assayed per HTS run, this hit rate still provides a fair number of active compounds.

Because HTS requires engagement in several cost- and labor-intensive techniques, many attempts have been made to increase its efficacy. As a consequence, in many companies, modelers have shifted their focus toward the design of libraries optimally suited for HTS. So-called 'optimally diverse libraries' showing a minimum of redundancy have been created and compiled on the basis of inventive, property-discriminating descriptors. However, the enrichment with respect to discovered hits did not significantly depart from a random selection taken from a large library holding various organic compounds in the correct molecular weight range [6]. Perhaps these studies have stimulated and improved our understanding of similarity and helped to design targeted libraries for one particular binding site or as isosteres for a given reference ligand. Similarity and likewise diversity are typical properties that can only be defined relative to a reference and not globally over an entire sample of compounds.

In the early stage of HTS quite optimistic and enthusiastic perspectives have been predicted. Together with the emergence of combinatorial chemistry, that was expected to push the frontiers of compound synthesis ahead by some orders of magnitude, the end of any rational and knowledge-based approaches has been forecasted. Today, several years later, a more realistic view has been accepted. First of all, automating biological testing is not without problems. False positives or non-specific target binding of possible test candidates are only some of the problems that puzzle scientists. Quite depressing are the reported success rates to translate apparent actives from HTS into leads that are suited for a subsequent optimization into a drug candidate [7]. Nevertheless, although hits discovered by HTS provide medicinal chemists with real chemical compounds that bind to a target [8], these hits do not contribute to our understanding of why and how they act upon the target. Any increase in knowledge is produced only once experimental structural biology or molecular modeling come into play to detect structural similarity or possible common binding modes among the obtained hits. Often enough hits are quite diverse in their chemical structure, thus preventing any reasonable intuitive comparison.

Virtual screening, VS, is an alternative where the selection of compounds with predicted binding properties is attempted in the computer [9]. The approach appears quite tempting. Compounds to be studied do not necessarily exist and their testing does not consume valuable substance material. Experimental deficiencies, e.g. due to limited solubility or other effects that can interfere with the assay conditions do not matter. In contrast to HTS, VS requires as key prerequisite knowledge about the criteria responsible for binding to a particular target. Either the three-dimensional structure of the target is given by crystal structure determination, by NMR and by homology modeling, or at least a rigid reference ligand with known bioactive conformation is known that allows for sophisticated pharmacophore modelling. This provides information about the binding-site geometry and helps to define and predict possible ligand-binding modes. Once the receptor-bound conformation of a reference ligand is known or can be estimated, searches for molecules with similar recognition properties, eventually experienced by quite different molecular skeletons, can be started. These comparative techniques either use fast flexible docking algorithms or focus on sophisticated molecular superposition techniques. However, if one sufficiently understands the features that make topologically diverse ligands similar or that are responsible for achieving a particular affinity toward a certain receptor, VS can be applied to screen either compound libraries of existing substances or computer-generated molecules. The latter examples could be detected as prospective leads and accordingly potential candidates for subsequent synthesis. Speculations have been made about the number of potential drug-like molecules (<500 Da) that could be synthesized, principally. Impressive numbers of 1060 up to 10200 compounds have been discussed [10]. Even the largest computer would not be sufficient to handle, screen or select such a vast universe of molecules. However, the computer provides the chance to generate molecules considering and reflecting properties that meet the criteria required for drug bicding, simultaneously satisfying the conditions for drug-likeness, e.g. defined by Lipinsky's rule of five [11]. Accordingly, virtual screening has to be seen in close connection to de novo design of individual compounds, but even more of medium size targeted libraries based on principles from combinatorial chemistry.

VS requires some key technologies. Docking and molecular superposi-tioning are the most important techniques to select candidate molecules. Both methods require a relevant and reliable scoring scheme to either predict the expected binding affinity toward a particular protein reference or to estimate the degree of similarity with a given reference ligand. Binding results when individually solvated molecules combine, usually shedding some of their association with solvent. Accordingly, solvation properties are of utmost importance for ligand binding and their understanding is a clear prerequisite to develop a successful scoring function.

As a kind of status report on the maturity of VS as a technique in drug design, the first workshop on new approaches in drug design and discovery was held in March 1999 at Schloß Rauischholzhausen close to Marburg in Germany [ 12]. More than 80 scientists gathered and discussed their experience with the different techniques. The speakers were invited to summarize their contributions together with their impression on the present applicability of their approach. Several of the speakers followed this request, which is summarized in this publication. The first two contributions, Combination of molecular similarity measures using data fusion by C.R. Ginn, P. Willett and J. Bradshaw, and Optimization of the drug-likeness of chemical libraries by J. Sadowski focus on the definition of similarity, in particular with respect to the selection and optimization of compound libraries also satisfying the general criteria for drug likeness. Since the prediction of binding affinity is of utmost importance in VS, the analysis of protein-ligand interactions is discussed in a special contribution of the York group by R.E. Hubbard, Th.G. Davies and J.R.H. Tame, Generating consistent sets of thermodynamic and structural data for analysis of protein-ligand interactions. Two contributions, one by C. Lemmen, M. Zimmermann and Th. Lengauer, Molecular superpositioning as an effective tool for virtual database screening, and one by M. Rarey and Th. Lengauer on A recursive algorithm for efficient combinatorial library docking describe two of the key techniques used to screen molecules whether they meet the similarity or binding-site criteria with a reference. Scoring of the results obtained by docking is essential in VS. Accordingly, several approaches have been summarized in this contribution.

M. Stahl, Modifications of the scoring function in FlexX for virtual screening applications, I. Muegge, A knowledge-based scoring function for protein-ligand interactions: Probing the reference state and H. Gohlke, M. Hendlich and G. Klebe, Predicting binding modes, binding affinities, and 'hot spots' for protein-ligand complexes using a knowledge-based scoring function describe new and improved scoring functions. The consideration in particular of solvation properties is described in the articles of N. Majeux, M. Scarsi, C. Tennette-Souaille and A. Caflisch, Hydrophobicity maps and docking of fragments with solvation and of V. Schnecke and L.A. Kuhn, Virtual screening with solvation and ligand-induced complementarity. A comparative study facing similarity methods with docking techniques is described by J. Mestres and R.M.A. Knegtel, Similarity versus docking in 3D virtual screening. An application to compose new potential leads from small components by computer screening is presented by T.J. Marrone, B.A. Luty and P.W. Rose, Discovering high affinity ligands from computationally predicted structures and affinities of small molecules bound to a target: A virtual screening approach, whereas H. Briem and U. Lessel try to find similarity criteria by docking or superposition methods: In vitro and In silico affinity fingerprints: Finding similarities beyond structural classes. A last section of the contributions focuses on the design of new compound libraries exploiting principles of combinational chemistry. In a contribution from J. Gasteiger, M. Pfortner, M. Sitzmann, R. Hollering, O. Sacher, Th. Kostka and N. Karg, Computer assisted synthesis and reaction planning in combinatorial chemistry, concepts from synthesis planning are applied to the development of compound libraries. V.J. Gillet and O. Nicolotti, Evaluation of reactant-based orproduct-based approaches to the design of combinatorial libraries describe criteria to design combinatorial libraries on the basis of properties of either the products or the reactants.

References

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Perspectives in Drug Discovery andDesign, 20: 1-16, 2000. KLUWER/ESCOM

© 2000 Kluwer Academic Publishers. Printed in the Netherlands.

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