Pharmacophore Pattern Application in Virtual Screening. Library Design and QSAR

Drug discovery and the use of computational approaches for infectious diseases

For centuries infectious diseases were the scourge of humanity, overcome only by the discovery of vaccination and penicillin. With an armamentarium of effective antibiotics, vaccines and drugs at hand, infectious diseases for many years were considered to be negligible. With the onset of the AIDS pandemic, the return of tuberculosis and influenza (e.g., swine influenza) this notion has changed in recent years. Drug discovery for infectious diseases, therefore, is again gaining increasing interest. This article discusses the drug-discovery process in this area and introduces major computational approaches used to identify suitable drug targets and to discover and optimize chemical lead compounds towards drug candidates using examples from antiparasitic drug discovery.

Informatics, machine learning and computational medicinal chemistry

This article reviews the use of informatics and computational chemistry methods in medicinal chemistry, with special consideration of how computational techniques can be adapted and extended to obtain more and higher-quality information. Special consideration is given to the computation of protein–ligand binding affinities, to the prediction of off-target bioactivities, bioactivity spectra and computational toxicology, and also to calculating absorption-, distribution-, metabolism- and excretion-relevant properties, such as solubility.

A Common Reference Framework for Analyzing/Comparing Proteins and Ligands. Fingerprints for Ligands And Proteins (FLAP): Theory and Application

A fast new algorithm (Fingerprints for Ligands And Proteins or FLAP) able to describe small molecules and protein structures using a common reference framework of four-point pharmacophore fingerprints and a molecular-cavity shape is described in detail. The procedure starts by using the GRID force field to calculate molecular interaction fields, which are then used to identify particular target locations where an energetic interaction with small molecular features would be very favorable. The target points thus calculated are then used by FLAP to build all possible four-point pharmacophores present in the given target site. A related approach can be applied to small molecules, using directly the GRID atom types to identify pharmacophoric features, and this complementary description of the target and ligand then leads to several novel applications. FLAP can be used for selectivity studies or similarity analyses in order to compare macromolecules without superposing them. Protein families can be compared and clustered into target classes, without bias from previous knowledge and without requiring protein superposition, alignment, or knowledge-based comparison. FLAP can be used effectively for ligand-based virtual screening and structure-based virtual screening, with the pharmacophore molecular recognition. Finally, the new method can calculate descriptors for chemometric analysis and can initiate a docking procedure. This paper presents the background to the new procedure and includes case studies illustrating several relevant applications of the new approach.

Descriptors you can count on? Normalized and filtered pharmacophore descriptors for virtual screening

The three-dimensional (3D) binary pharmacophore fingerprints find wide application as descriptors in applications ranging from virtual screening through library design. While the 3D content they capture is an intuitively attractive feature of such measures, maximizing their signal to noise ratio has proven to be a tricky balancing act. This issue surfaces primarily due to the potential of such fingerprints to create an explosion of pharmacophores as molecular complexity and flexibility increases. In this article, we describe a modification to the fingerprint generation process that normalizes pharmacophore occurrence frequency by the conformational ensemble size used to derive the descriptor. By including pharmacophore frequency and conformational count, the importance of a given pharmacophore is weighted by the probability of its existence within a given conformational ensemble, rather than treating each pharmacophore equally. In addition, a number of filters have been added to permit the removal of unwanted pharmacophores from the descriptor set. These filters are based on pharmacophore composition (e.g. permutations made up primarily of lipophilic and/or aromatic centers), and size (pharmacophore perimeter length relative to the largest perimeter length found in the molecule). The highly uneven nature of pharmacophore distributions across the conformational ensemble used to generate them is highlighted, as are enrichment comparisons with their binary fingerprint peers. In addition, the limitations in descriptor comparison validation are high-lighted as an illustration of the need for more extensive validation experiments.

QSAR and QSPR based solely on surface properties?

The use of descriptors based on local properties calculated at the molecular surface for QSPR models is discussed. It is suggested that descriptors should be related to the physical theory of intermolecular interactions and the relationship between established surface-based descriptors and the fundamental types of intermolecular interaction is discussed. Descriptors based on local properties that do not encode the chemical constitution of the molecule directly are likely to provide less local QSPR models and favor scaffold hopping. The major disadvantage for surface-based descriptors is that they are difficult to interpret in the sense of relating predictions to the chemical composition of the molecule. This disadvantage must be alleviated by suitable model-interrogation techniques.

Analysis and optimization of structure-based virtual screening protocols. 2. Examination of docked ligand orientation sampling methodology: mapping a pharmacophore for success

An important element of any structure-based virtual screening (SVS) technique is the method used to orient the ligands in the target active site. This has been a somewhat overlooked issue in recent SVS validation studies, with the assumption being made that the performance of an algorithm for a given set of orientation sampling settings will be representative for the general behavior of said technique. Here, we analyze five different SVS targets using a variety of sampling paradigms within the DOCK, GOLD and PROMETHEUS programs over a data set of approximately 10,000 noise compounds, combined with data sets containing multiple active compounds. These sets have been broken down by chemotype, with chemotype hit rate used to provide a measure of enrichment with a potentially improved relevance to real world SVS experiments. The variability in enrichment results produced by different sampling paradigms is illustrated, as is the utility of using pharmacophores to constrain sampling to regions that reflect known structural biology. The difference in results when comparing chemotype with compound hit rates is also highlighted.