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The concept of ligand potency is briefly discussed here as well as why this is still a challenge for its complete comprehension. In this context, we explain also the meaning of ligand efficiency (LE), which has been greeted with both enthusiasm and criticism among the drug design audience. A full understanding of LE requires the complex interpretation of the potency concept presenting the uncertainty similar to this of the Zeno paradox. In reality, the efficiency of LE is caused by the high degree of preference for slim pharma drug candidates., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Opportunistic fungi are the most important pathogens in modern world. They are responsible for severe infections in majority of immunocompromised patients. These microorganisms are commonly present in our environment which is natural reservoir of new, resistant species. For this reason mycoses are mainly chronic or long-lasting diseases. Our arsenal of antifungal drugs is growing but still insufficient for emerging resistant pathogens. An alternative for novel chemical entity drugs is the multidrug approach. This exploiting the drugs being currently on market applying simultaneously for better efficacy or to eradicate resistance. Synergy is the term that describes the phenomenon of increased potency of two or more drugs administered in combination. In the last decades it gains more interest and numbers of synergy claimed reports is growing exponentially. However these have rather low impact on clinical trials or practical use of antimycotics. In present review we wish to discuss current status of synergy between antifungal drugs. Both theoretical point of view and practical applicability in clinical terms are covered. There are serious differences between the assumptions, methods and interpretations of the results and sometimes even obvious mistakes in the procedure that was applied or in the outcomes discussed. On the other hands the specificity of fungal infections introduce dozens of factors affecting the observed results. Shift form in vitro studies to clinical trials reveals further difficulties. Hopefully multi-drug approach seems to be effective even if no strong synergy is displayed.

An artificial polypeptide receptor (APR) library was created by using the self-organization of N-lipidated peptides attached to cellulose via m-aminophenylamino-1,3,5-triazine. The response of the library was probed using a series of novel H3 receptor ligands. Since no guidelines on how to design an APRs selective vs certain receptor types exist, a diverse set of amino acids (Ala, Trp, Pro, Glu, His, Lys and Ser) were used and coupled with one of three gating fatty acids (palmitic, ricinoleic or capric). A competitive adsorption-desorption of an appropriate reporter dye was used for the indirect visualization of the interactions of guests with particular receptors. The resulted library response to individual inhibitors was then arranged in a matrix, preprocessed and analyzed using the principal component analysis (PCA) and partial least squares (PLS) method. The most important conclusion obtained from the PCA analysis is that the library differentiates the probed compounds according to the lipophilicity of the gating unit. The PC3 with a dominant absolute contribution of the receptors containing Glu allowed for the best separation of the ligands with respect to their activity. This conclusion is in agreement with the fact that Glu 206 is a genuine ligand counterpart in the natural histamine receptor.

A comparative structure-affinity study of anthraquinone dyes adsorption on cellulose fibre is presented in this paper. We used receptor-dependent 4D-QSAR methods based on grid and neural (SOM) methodology coupled with IVEPLS procedure. The applied RD 4D-QSAR approach focuses mainly on the ability of mapping dye properties to verify the concept of tinctophore in dye chemistry. Moreover, the stochastic SMV procedure to investigate the predictive ability of the method for a large population of 4D-QSAR models was employed. The obtained findings were compared with the previously published RI 3D/4D-QSAR models for the corresponding anthraquinone trainings sets. The neutral (protonated) and anionic (deprotonated) forms of anthraquinone scaffold were examined in order to deal with the uncertainty of the dye ionization state. The results are comparable to both the neutral and anionic dye sets regardless of the occupancy and charge descriptors applied, respectively. It is worth noting that the SOM-4D-QSAR behaves comparably to the cubic counterpart which is observed in each training/test subset specification (4D-QSAR-Jo vs SOM- 4D-QSARo and 4D-QSAR-Jq vs SOM-4D-QSARq). Additionally, an attempt was made to specify a common set of variables contributing significantly to dye-fiber binding affinity; it was simultaneously performed for some arbitrary chosen SMV models. The presented RD 4D-QSAR methodology together with IVE-PLS procedure provides a robust and predictive modeling technique, which facilitates detailed specification of the molecular motifs significantly contributing to the fiber-dye affinity.

Fragmental topology-activity landscapes (FRAGTAL), a new concept for encoding molecular descriptors for fragonomics into the framework of the molecular database records is presented in this paper. Thus, a structural repository containing biological activity data was searched in a substructure mode by a series of molecular fragments constructed in an incremental or decremental manner. The resulted series of database hits annotated with their activities construct FRAGTAL descriptors encoding a frequency of the certain fragments among active compounds and/or their activities. Actually, this method might be interpreted as a simplified adaptation of the frequent subgraph mining (FSM) method. The FRAGTAL method reconstructs the way in which medicinal chemists are used to designing a prospective drug structure intuitively. A representative example of the practical application of FRAGTAL within the ChemDB Anti-HIV/OI/TB database for disclosing new fragments for HIV-1 integrase inhibition is discussed. In particular, FRAGTAL method identifies ethyl malonate amide (EMA) as the diketo acid (DKA) related arrangement. Since new molecular constructs based on the EMA fragment are still a matter of future investigations we referred to this as anthe DKA offspring.

The concept of privileged structures/substructures (PS) is the idea that certain structural features produce biological effects more often than others. The PS method can be seen as an offspring of fragonomics, which is based on recent experimental measurements of protein-ligand interactions. If PS prove to be true, then chemical motives that enrich biological activity can be used when designing new drugs. However, PS remain controversial because we cannot be sure whether the excess of active structures does not result from an abundance in chemical libraries. In this review, we will focus, in particular, on the preferential organization of azanaphthalene scaffolds (AN) in drugs and natural products (NP), which are preferred by Nature in evolution. We will show that knowledge discovery in molecular databases can reveal interesting time-trends profiles for important classes of potentially privileged scaffolds. The chemical library of AN is dominated by monoaza-compounds, among which quinoline appears to be the most frequently investigated scaffold; however; more sophisticated database mining seems to indicate different PS patterns within the AN scaffold family.

A detailed knowledge of hydrogen bond geometry and its directional preferences is vital for in silico investigations of the ligand-receptor short-range non-covalent interactions. The spatial arrangement of the carbonyl and hydroxyl groups seems to determine the capability of β-ketoenol derivatives to recognize the surrounding environment by forming inter- and intra-molecular hydrogen bonds (IHB). In the current study we examined the application of the MoStBioDat platform for a massive database screening of the IHB motifs in β-ketoenol subunits (O=C-C=C-OH). Then, the virtual 3D structural data derived from ZINC and PubChem repository were compared to the experimentally determined CSD data. Differences specific for each database were discovered, which indicated inaccuracies in the simulated data.

Photodynamic therapy (PDT), the concept of cancer treatment through the selective uptake of a light-sensitive agent followed by exposure to a specific wavelength, is limited by the transport of a photosensitizer (PS) to the tumor tissue. Porphyrin, an important PS class, can be used in PDT in the form of its prodrug molecule 5-aminolevulinic acid (5-ALA). Unfortunately, its poor pharmacokinetic properties make this compound difficult to administer. Two different methods for eliminating this problem can be distinguished. The first approach is to play with its formulation in order to improve the drug's applicability. The second approach, which is to find possible 5- ALA prodrugs, is an example of the double-prodrug method, a strategy often used in modern drug design. In this approach, the biological mechanisms in a long biosynthetic pathway involving several steps must be completed before the active drug appears. Recently, an idea of enhancing PDT sensitization using the so-called iron chelators seemed to increase the accumulation of protoporphyrin in cells. At the same time, iron chelators can destroy tumor cells by producing active oxygen after the formation of an active drug by chelating iron in the cancer cells. Thus, in the latter case, the therapy resembles a prodrug strategy. The mechanism can be explained by the Fenton reaction. Vitamin C is another example of a potential anticancer agent of this type.

Computer simulations play a crucial role in contemporary chemical investigations, generating enormous amounts of data. The constraint of sharing data and results is regarded as a major impediment in drug discovery. Among the steepest barriers to overcome in the high throughput screening studies is the limited number of suitable, freely accessible repositories for storing drug and drug target data. By offering a uniform data storage and retrieval mechanism, various data might be compared and exchanged easily. This paper presents the stages of the MoStBioDat software platform development, originally designed for the efficient storage, management and access of SDF and PDB data. The detailed architecture and software implementation of this project are described, indicating also the disadvantages of the solutions chosen. The current implementation of the first prototype is written in Python, an open-source, high-level, object-oriented scripting language. The modular architecture of the package enables future extension with the necessary functionalities. The main objective of the MoStBioDat is to serve as an alternative, extensible open-source database derived partly from SDF and PDB files.

Although the assortment of antifungal drugs is broad, the most commonly used agents have major drawbacks. Toxicity, serious side effects or the emergence of drug resistance are amongst them. New drugs and drug candidates under clinical trials do not guarantee better pharmacological parameters. These new medicines may appear effective; however; they may cause serious side effects. This current review is focused on the recent findings in the design of quinoline based antifungal agents. This field seems to be especially interesting as 8-hydroxyquinoline and its metal complexes have been well known as antifungals for years. Structural similarities between quinoline based antifungals and allylamines or homoallylamines, e.g. terbinafine is another interesting fact. Quinoline can be identified in a number of synthetic and natural antifungals, which indicates nature's preference for this fragment and identifying it as one of the so-called privileged structures. We have discussed new trends in the design of quinolines with antifungal properties, their possible targets and the structure activity relationships within the antifungal series developed.

Quantitative Structure Activity Relationship (QSAR) is an approach of mapping chemical structure to properties. A significant development can be observed in the last two decades in this method which originated from the Hansch analysis based on the logP data and Hammett constant towards a growing importance of the molecular descriptors derived from 3D structure including conformational dynamics and solvation scenarios. However, molecular interactions in biological systems are complex phenomena generating extremely noisy data, if simulated in silico. This decides that activity modeling and predictions are a risky business. Molecular recognition uncertainty in traditional receptor independent (RI) m-QSAR cannot be eliminated but by the inclusion of the receptor data. Modeling ligand-receptor interactions is a complex computational problem. This has limited the development of the receptor dependent (RD) m-QSAR. However, a steady increase of computational power has also improved modeling ability in chemoinformatics and novel RD QSAR methods appeared. Following the RI m-QSAR terminology this is usually classified as RD 3/6D-QSAR. However, a clear systematic m-QSAR classification can be proposed, where dimension m refers to, the static ligand representation (3D), multiple ligand representation (4D), ligand-based virtual or pseudo receptor models (5D), multiple solvation scenarios (6D) and real receptor or target-based receptor model data (7D).

We used comparative molecular surface analysis to design molecules for the synthesis as part of the search for new HIV-1 integrase inhibitors. We analyzed the virtual combinatorial library (VCL) constituted from various moieties of styrylquinoline and styrylquinazoline inhibitors. Since imines can be applied in a strategy of dynamic combinatorial chemistry (DCC), we also tested similar compounds in which the -C=N- or -N=C- linker connected the heteroaromatic and aromatic moieties. We then used principal component analysis (PCA) or self-organizing maps (SOM), namely, the Kohonen neural networks to obtain a clustering plot analyzing the diversity of the VCL formed. Previously synthesized compounds of known activity, used as molecular probes, were projected onto this plot, which provided a set of promising virtual drugs. Moreover, we further modified the above mentioned VCL to include the single bond linker -C-N- or -N-C-. This allowed increasing compound stability but expanded also the diversity between the available molecular probes and virtual targets. The application of the CoMSA with SOM indicated important differences between such compounds and active molecular probes. We synthesized such compounds to verify the computational predictions.

In the current work we investigated 3D-QSAR data by the use of the coupled leave-several-out (LSO) and leave-one-out (LOO) cross-validation (CV) procedures. We verified the above mentioned scheme using both simulated data and real 3D QSAR data describing a series of CoMFA steroids, heterocyclic azo dyes and styrylquinoline HIV integrase inhibitors. Unlike in standard analyses, this technique characterizes individual method not by a single performance metrics but screens a whole possible modeling space by sampling different molecules into the training and test sets, respectively. This allowed us for the discussion of the information included in the estimators validating cross-validation procedures, as well as the comparison of the efficiency of several 3D QSAR schemes, in particular, Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Surface Analysis (CoMSA). Moreover, it allows one to acquire some general knowledge about predictive and modeling ability in 3D QSAR method.

The use of Kohonen feature maps for the visualization of various aspects of molecular similarity is briefly reviewed and illustrated. It is shown that a specific feature of self-organizing maps (SOM) makes them of special interest for the screening of compounds. In particular, these methods were used to design candidates for new sweeteners, which were then synthesized.