Virtual screening leads to the discovery of an effective antagonist of lymphocyte function-associated antigen-1.

The binding of lymphocyte function-associated antigen-1 (LFA-1) to its ligand on endothelial cells, intercellular adhesion molecule-1 (ICAM-1), is a crucial step in the migration of leukocytes during the early stages of inflammation and is also involved in T-cell activation. In this paper, we report the identification of a series of novel antagonists of the LFA-1/ICAM-1 interaction using ligand-based virtual screening (VS), analogue design, and structure-activity relationship (SAR) analysis. Candidate compounds were evaluated in protein binding and cell adhesion assays. Experimental evaluation of only 25 candidates selected from a pool of approximately 2.5 million database compounds identified an initial hit that could be expanded and converted into a lead that effectively blocked the interaction between LFA-1 and ICAM-1.

Anatomy of fingerprint search calculations on structurally diverse sets of active compounds.

Similarity searching using molecular fingerprints is a widely used approach for the identification of novel hits. A fingerprint search involves many pairwise comparisons of bit string representations of known active molecules with those precomputed for database compounds. Bit string overlap, as evaluated by various similarity metrics, is used as a measure of molecular similarity. Results of a number of studies focusing on fingerprints suggest that it is difficult, if not impossible, to develop generally applicable search parameters and strategies, irrespective of the compound classes under investigation. Rather, more or less, each individual search problem requires an adjustment of calculation conditions. Thus, there is a need for diagnostic tools to analyze fingerprint-based similarity searching. We report an analysis of fingerprint search calculations on different sets of structurally diverse active compounds. Calculations on five biological activity classes were carried out with two fingerprints in two compound source databases, and the results were analyzed in histograms. Tanimoto coefficient (Tc) value ranges where active compounds were detected were compared to the distribution of Tc values in the database. The analysis revealed that compound class-specific effects strongly influenced the outcome of these fingerprint calculations. Among the five diverse compound sets studied, very different search results were obtained. The analysis described here can be applied to determine Tc intervals where scaffold hopping occurs. It can also be used to benchmark fingerprint calculations or estimate their probability of success.

New methodologies for ligand-based virtual screening.

Computational screening of compound databases has become increasingly popular in pharmaceutical research. Virtual screening approaches can roughly be divided into target structure-based screening (often referred to as docking) and screening using active compounds as templates (ligand-based virtual screening). Ligand-based screening techniques essentially focus on comparative molecular similarity analysis of compounds with known and unknown activity, regardless of the methods or algorithms used. In this review, we first provide an overview of widely used ligand-based virtual screening approaches including various database filters and then discuss recent trends in this field and new methodological developments.

Similarity search profiling reveals effects of fingerprint scaling in virtual screening.

Fingerprint scaling is a method to increase the performance of similarity search calculations. It is based on the detection of bit patterns in keyed fingerprints that are signatures of specific compound classes. Application of scaling factors to consensus bits that are mostly set on emphasizes signature bit patterns during similarity searching and has been shown to improve search results for different fingerprints. Similarity search profiling has recently been introduced as a method to analyze similarity search calculations. Profiles separately monitor correctly identified hits and other detected database compounds as a function of similarity threshold values and make it possible to estimate whether virtual screening calculations can be successful or to evaluate why they fail. This similarity search profile technique has been applied here to study fingerprint scaling in detail and better understand effects that are responsible for its performance. In particular, we have focused on the qualitative and quantitative analysis of similarity search profiles under scaling conditions. Therefore, we have carried out systematic similarity search calculations for 23 biological activity classes under scaling conditions over a wide range of scaling factors in a compound database containing approximately 1.3 million molecules and monitored these calculations in similarity search profiles. Analysis of these profiles confirmed increases in hit rates as a consequence of scaling and revealed that scaling influences similarity search calculations in different ways. Based on scaled similarity search profiles, compound sets could be divided into different categories. In a number of cases, increases in search performance under scaling conditions were due to a more significant relative increase in correctly identified hits than detected false-positives. This was also consistent with the finding that preferred similarity threshold values increased due to fingerprint scaling, which was well illustrated by similarity search profiling.

POT-DMC: A virtual screening method for the identification of potent hits.

A method for ligand-based virtual screening (LBVS), dynamic mapping of consensus positions (DMC), has been extended to take different potency levels of template compounds into account. This potency scaling technique is designed to tune search calculations toward the detection of increasingly potent hits. LBVS analysis of three different compound classes confirmed the ability of potency-scaled DMC (POT-DMC) to identify active database compounds with higher potency than conventional calculations.

Identification of structurally diverse growth hormone secretagogue agonists by virtual screening and structure-activity relationship analysis of 2-formylaminoacetamide derivatives.

Two molecules with known growth hormone secretagogue (GHS) agonist activity were used as templates to computationally screen approximately 80000 compounds. A total of 108 candidate compounds were selected, and five of them were found to be active in the low-micromolar range in both cell-based and direct binding assays. These compounds were structurally diverse and significantly differed from known GHS agonists. The most active compound was subjected to SAR evaluation, which slightly increased its potency and identified molecular regions important for specific GHS agonist activity.

Similarity search profiles as a diagnostic tool for the analysis of virtual screening calculations.

An analysis method termed similarity search profiling has been developed to evaluate fingerprint-based virtual screening calculations. The analysis is based on systematic similarity search calculations using multiple template compounds over the entire value range of a similarity coefficient. In graphical representations, numbers of correctly identified hits and other detected database compounds are separately monitored. The resulting profiles make it possible to determine whether a virtual screening trial can in principle succeed for a given compound class, search tool, similarity metric, and selection criterion. As a test case, we have analyzed virtual screening calculations using a recently designed fingerprint on 23 different biological activity classes in a compound source database containing approximately 1.3 million molecules. Based on our predefined selection criteria, we found that virtual screening analysis was successful for 19 of 23 compound classes. Profile analysis also makes it possible to determine compound class-specific similarity threshold values for similarity searching.

Virtual screening methods that complement HTS.

In this review, we discuss a number of computational methods that have been developed or adapted for molecule classification and virtual screening (VS) of compound databases. In particular, we focus on approaches that are complementary to high-throughput screening (HTS). The discussion is limited to VS methods that operate at the small molecular level, which is often called ligand-based VS (LBVS), and does not take into account docking algorithms or other structure-based screening tools. We describe areas that greatly benefit from combining virtual and biological screening and discuss computational methods that are most suitable to contribute to the integration of screening technologies. Relevant approaches range from established methods such as clustering or similarity searching to techniques that have only recently been introduced for LBVS applications such as statistical methods or support vector machines. Finally, we discuss a number of representative applications at the interface between VS and HTS.

Cell-based partitioning.

Partitioning techniques are widely used to classify compound sets or databases according to specific chemical or biological criteria. Partitioning is conceptually related to, yet algorithmically distinct from, conventional clustering methods and is particularly suitable for efficient processing of very large compound sets. Currently, some of the most popular partitioning approaches in the chemoinformatics field involve dimension reduction of initially defined chemistry spaces and creation of subsections of low-dimensional space for molecular classification. These subsections are often called cells. Original chemical reference spaces are generated through selection of various descriptors of molecular structure and properties. Principles and methodological aspects of dimension reduction of chemical spaces and compound partitioning in low-dimensional space are described herein.

Molecular similarity analysis and virtual screening by mapping of consensus positions in binary-transformed chemical descriptor spaces with variable dimensionality.

A novel compound classification algorithm is described that operates in binary molecular descriptor spaces and groups active compounds together in a computationally highly efficient manner. The method involves the transformation of continuous descriptor value ranges into a binary format, subsequent definition of simplified descriptor spaces, identification of consensus positions of specific compound sets in these spaces, and iterative adjustments of the dimensionality of the descriptor spaces in order to discriminate compounds sharing similar activity from others. We term this approach Dynamic Mapping of Consensus positions (DMC) because the definition of reference spaces is tuned toward specific compound classes and their dimensionality is increased as the analysis proceeds. When applied to virtual screening, sets of bait compounds are added to a large screening database to identify hidden active molecules. In these calculations, molecules that map to consensus positions after elimination of most of the database compounds are considered hit candidates. In a benchmark study on five biological activity classes, hits for randomly assembled sets of bait molecules were correctly identified in 95% of virtual screening calculations in a source database containing more than 1.3 million molecules, thus providing a measure of the sensitivity of the DMC technique.

Design and evaluation of a molecular fingerprint involving the transformation of property descriptor values into a binary classification scheme.

A new fingerprint design concept is introduced that transforms molecular property descriptors into two-state descriptors and thus permits binary encoding. This transformation is based on the calculation of statistical medians of descriptor distributions in large compound collections and alleviates the need for value range encoding of these descriptors. For binary encoded property descriptors, bit positions that are set off capture as much information as bit positions that are set on, different from conventional fingerprint representations. Accordingly, a variant of the Tanimoto coefficient has been defined for comparison of these fingerprints. Following our design idea, a prototypic fingerprint termed MP-MFP was implemented by combining 61 binary encoded property descriptors with 110 structural fragment-type descriptors. The performance of this fingerprint was evaluated in systematic similarity search calculations in a database containing 549 molecules belonging to 38 different activity classes and 5000 background molecules. In these calculations, MP-MFP correctly recognized approximately 34% of all similarity relationships, with only 0.04% false positives, and performed better than previous designs and MACCS keys. The results suggest that combinations of simplified two-state property descriptors have predictive value in the analysis of molecular similarity.

Profile scaling increases the similarity search performance of molecular fingerprints containing numerical descriptors and structural keys.

The concept of compound class-specific profiling and scaling of molecular fingerprints for similarity searching is discussed and applied to newly designed fingerprint representations. The approach is based on the analysis of characteristic patterns of bits in keyed fingerprints that are set on in compounds having equivalent biological activity. Once a fingerprint profile is generated for a particular activity class, scaling factors that are weighted according to observed bit frequencies are applied to signature bit positions when searching for similar compounds. In systematic similarity search calculations over 23 diverse activity classes, profile scaling consistently increased the performance of fingerprints containing property descriptors and/or structural keys. A significant improvement of approximately 15% was observed for a new fingerprint consisting of binary encoded molecular property descriptors and structural keys. Under scaling conditions, this fingerprint, termed MP-MFP, correctly recognized on average close to 60% of all active test compounds, with only a few false positives. MP-MFP outperformed MACCS keys and other reference fingerprints. In general, optimum performance in scaling calculations was achieved at higher threshold values of the Tanimoto coefficient than in nonscaled calculations, thereby increasing the search selectivity. In general, putting relatively high weight on signature bit positions that were always, or almost always, set on was found to be the most effective scaling procedure. Analysis of class-specific search performance revealed that profile scaling of MP-MFP improved the similarity search results for each of the 23 activity classes.

Partitioning methods for the identification of active molecules.

The dramatically increasing number of compounds that become available for biological evaluation presents a significant challenge for database design, management, and mining. Computational approaches for screening, profiling, or filtering of large compound collections are by now widely used in pharmaceutical research. Among popular compound classification and database mining techniques, partitioning methods are computationally very efficient and particularly suitable for the analysis of increasingly large molecular databases, as they do not depend on pair-wise comparisons of compounds to assess molecular similarity or diversity. Promising applications of partitioning algorithms include diversity selection, searching for compounds with desired biological activity, or the derivation of predictive models from screening datasets. Compound partitioning is introduced here in the context of virtual screening and different partitioning methods are discussed that operate in low-dimensional or other chemical descriptor spaces, including a number of practical drug-discovery-related applications.

Median Partitioning: a novel method for the selection of representative subsets from large compound pools.

A method termed Median Partitioning (MP) has been developed to select diverse sets of molecules from large compound pools. Unlike many other methods for subset selection, the MP approach does not depend on pairwise comparison of molecules and can therefore be applied to very large compound collections. The only time limiting step is the calculation of molecular descriptors for database compounds. MP employs arrays of property descriptors with little correlation to divide large compound pools into partitions from which representative molecules can be selected. In each of n subsequent steps, a population of molecules is divided into subpopulations above and below the median value of a property descriptor until a desired number of 2n partitions are obtained. For descriptor evaluation and selection, an entropy formulation was embedded in a genetic algorithm. MP has been applied here to generate a subset of the Available Chemicals Directory, and the results have been compared with cell-based partitioning.

Bio- and chemo-informatics beyond data management: crucial challenges and future opportunities.

Bio- and chemo-informatics are now thought to be crucial to the success and integration of biotechnology and drug discovery. Research in this area has expanded to go beyond data- and information-management. Here, we review exemplary areas, such as target identification and validation, virtual screening, and prediction of downstream characteristics of leads, where further research will play a key role in progressing the field.

Methods for compound selection focused on hits and application in drug discovery.

In the context of virtual screening calculations, a multiple fingerprint-based metric is applied to generate focused compound libraries by database searching. Different fingerprints are used to facilitate a similarity step for database mining, followed by a diversity step to assemble the final library. The method is applied, for example, to build libraries of limited size for hit-to-lead development efforts. In studies designed to inhibit a therapeutically relevant protein-protein interaction, small molecular hits were initially obtained by combined fingerprint- and structure-based virtual screening and used for the design of focused libraries. We review the applied virtual screening approach and report the statistics and results of screening as well as focused library design. While the structures of lead compounds cannot be disclosed, the analysis is thought to provide an example of the interplay of different methods applied in practical lead identification.

Differential Shannon entropy analysis identifies molecular property descriptors that predict aqueous solubility of synthetic compounds with high accuracy in binary QSAR calculations.

Prediction of aqueous solubility of organic molecules by binary QSAR was used as a test case for a recently introduced entropy-based descriptor selection method. Property descriptors suitable for solubility predictions were exclusively selected on the basis of Shannon entropy calculations in molecular learning sets, not taking any other information into account. Sets of only five or 10 2D descriptors with largest entropy differences between molecules above or below a defined solubility threshold yielded consistently high prediction accuracy between 80% and 90% in binary QSAR calculations, regardless of the threshold values applied. The top five descriptors with largest differential Shannon entropy (DSE) values achieved an average prediction accuracy of 88%. These findings suggest that differences in entropy and relative information content of descriptors in compared compound data sets correlate with significant differences in physical properties and support the practical relevance of entropy-based descriptor selection routines. The study also demonstrates that binary QSAR methodology can be effectively used to classify small molecules according to aqueous solubility.