Andrzej Bak

Modeling robust QSAR

Quantitative Structure Activity Relationship (QSAR) is a term describing a variety of approaches that are of substantial interest for chemistry. This method can be defined as indirect molecular design by the iterative sampling of the chemical compounds space to optimize a certain property and thus indirectly design the molecular structure having this property. However, modeling the interactions of chemical molecules in biological systems provides highly noisy data, which make predictions a roulette risk. In this paper we briefly review the origins for this noise, particularly in multidimensional QSAR. This was classified as the data, superimposition, molecular similarity, conformational, and molecular recognition noise. We also indicated possible robust answers that can improve modeling and predictive ability of QSAR, especially the self-organizing mapping of molecular objects, in particular, the molecular surfaces, a method that was brought into chemistry by Gasteiger and Zupan.

Privileged Structures - Dream or Reality: Preferential Organization of Azanaphthalene Scaffold

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.

The comparative molecular surface analysis (COMSA): A nongrid 3D QSAR method by a coupled neural network and PLS system: Predicting pKa values of benzoic and alkanoic acids

A self-organizing neural network was used to design a novel method capable of the quantitative prediction of molecular properties. The method is based on the comparison of molecular surfaces performed by the coupled neural network and PLS system. Unlike CoMFA and related methods it does not compare the properties describing a discrete set of points but the average property values calculated for a certain area of the molecular surface. It has been found that the results of the PLS analysis of the series of the comparative matrices of the molecular electrostatic potential (MEP) are quite stable. Also the results only slightly depend on such parameters as the number of points sampled at the molecular surface (D) or a winning distance (MD) of the self-organizing neurons. The influence of these parameters for modeling the effects limited by steric and electronic effects was determined and the pK(a) values of the ortho-, meta-, and para- (o-, m-, p-) analogues of benzoic acid and selected alkanoic acids were predicted. We generally found that for the series analyzed CoMSA gave better models than CoMFA.

Probability Issues in Molecular Design: Predictive and Modeling Ability in 3D-QSAR Schemes

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.

Modeling Steric and Electronic Effects in 3D- and 4D-QSAR Schemes: Predicting Benzoic pKa Values and Steroid CBG Binding Affinities

We conducted a systematic study of the performance of the 3D- and 4D-QSAR schemes in modeling steric and electronic effects. In particular, we compared the CoMFA and Hopfingers 4D-QSAR schemes, which apply completely different concepts for the generation of the molecular data used for modeling QSAR. Hence, we attempted to predict the pK(a) values of (o-, m-, and p-)benzoic acids which were divided into three subseries in order to simulate different levels of steric and electronic control. The steroids binding to CBG were used as a benchmark series where biological activity is limited by shape factors. Although individual models differ depending upon the individual scheme, generally, both CoMFA and 4D-QSAR appeared to provide comparable results, irrespective of the differences in the coding schemes used for the description. Moreover, a new 4D-QSAR scheme involving a self-organizing neural network was designed. Generally, the SOM scheme that we designed performs comparably to the grid scheme; however, it provides better results for the charge type descriptors, and the robust neuron architecture allows for the decrease of the influence of the molecular superimposition mode.

Self-organizing Neural Networks for Modeling Robust 3D and 4D QSAR: Application to Dihydrofolate Reductase Inhibitors

We have used SOM and grid 3D and 4D QSAR schemes for modeling the activity of a series of dihydrofolate reductase inhibitors. Careful analysis of the performance and external predictivities proves that this method can provide an efficient inhibition model.

Comparative molecular surface analysis (CoMSA) for virtual combinatorial library screening of styrylquinoline HIV-1 blocking agents.

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.

Structure-Based Modeling of Dye-Fiber Affinity with SOM-4D-QSAR Paradigm: Application to Set of Anthraquinone Derivatives

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.

Pharmacophore-based database mining for probing fragmental drug-likeness of diketo acid analogues

A number of the structurally diverse chemical compounds with functional diketo acid (DKA) subunit(s) have been revealed by combined online and MoStBiodat 3D pharmacophore-guided ZINC and PubChem database screening. We used the structural data available from such screening to analyse the similarities of the compounds containing the DKA fragment. Generally, the analysis by principal component analysis and self-organizing neural network approaches reveals four families of compounds complying with the chemical constitution (aromatic, aliphatic) of the compounds. From a practical point of view, similar studies may reveal potential bioisosteres of known drugs, e.g. raltegravir/elvitegravir. In this context, it seems that mono-halogenated aryl substructures with para group show the closest similarity to these compounds, in contrast to structures where the aromatic ring is halogenated in both ortho- and para-locations.

Ethyl malonate amides: A diketo acid offspring fragment for HIV integrase inhibition

While searching for new HIV integrase inhibitors we discovered that some ethyl malonate amides (EMA) are active against this enzyme. Surprisingly, the main function can only very rarely be found among the reported drug candidates. We synthesised a series of compounds in order to establish and analyse the structure-activity relationship. The similarity to the important classes of HIV integrase inhibitors as well as the synthetic availability of the different targets including this pharmacophore makes EMA compounds an interesting object of investigations.