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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.