Molecular Property Descriptors Research Articles

A chemometric study of megazol derivatives with activity against Trypanosoma equiperdum

The AM1 semiempirical method was employed to study megazol and 13 of its analogues where their activity against Trypanosoma equiperdum was obtained from in vitro tests. Several molecular properties (descriptors or variables) were calculated for the 14 compounds studied to be correlated with the biological activity. For a practical analysis of large data sets, it is necessary to reduce the dimensionality and select the most relevant descriptors related to the biological activity under study. For this purpose, the following chemometric methods were employed: principal component analysis (PCA), hierarchical cluster analysis (HCA), K-nearest neighbour (KNN), stepwise discriminant analysis (SDA) and soft independent modelling of class analogy (SIMCA). These methods showed that the descriptors molecular electronic energy (Eelet), charge on the first nitrogen at substituent 2 (qN), volume of substituent at C5 position (V-S5), dihedral angle (D3) and bond length between atom C4 and its substituents (L4) are responsible for the separation between active and inactive compounds against T. equiperdum.

Chemical Database Mining through Entropy-Based Molecular Similarity Assessment of Randomly Generated Structural Fragment Populations

We describe a novel approach to search for active compounds that is based on the generation of random molecular fragment populations. As a similarity-based methodology, fragment profiling does not depend on the use of predefined descriptors of molecular structure and properties and the design of chemical space representations. To adapt the generation and comparison of random fragment populations for large-scale compound screening, we compare different fragmentation schemes, introduce the concept of compound class-specific fragment frequencies, and develop a novel entropic similarity metric for compound ranking. The approach has been extensively tested on 15 different compound activity classes with varying degrees of intraclass structural diversity and produced promising results in these calculations, comparable to similarity searching using fingerprints. A key feature of fragment profile searching is that the calculation of compound class-specific proportional Shannon entropy of random fragment distributions enables the identification of database molecules that share a significant number of signature substructures with known active compounds.

Quantum chemical and statistical study of megazol‐derived compounds with trypanocidal activity

AbstractIn this work we performed a structure–activity relationship (SAR) study with the aim to correlate molecular properties of the megazol compound and 10 of its analogs with the biological activity against Trypanosoma cruzi (trypanocidal or antichagasic activity) presented by these molecules. The biological activity indication was obtained from in vitro tests and the molecular properties (variables or descriptors) were obtained from the optimized chemical structures by using the PM3 semiempirical method. It was calculated ∼80 molecular properties selected among steric, constitutional, electronic, and lipophilicity properties. In order to reduce dimensionality and investigate which subset of variables (descriptors) would be more effective in classifying the compounds studied, according to their degree of trypanocidal activity, we employed statistical methodologies (pattern recognition and classification techniques) such as principal component analysis (PCA), hierarchical cluster analysis (HCA), K‐nearest neighbor (KNN), and discriminant function analysis (DFA). These methods showed that the descriptors molecular mass (MM), energy of the second lowest unoccupied molecular orbital (LUMO+1), charge on the first nitrogen at substituent 2 (qN′), dihedral angles (D1 and D2), bond length between atom C4 and its substituent (L4), Moriguchi octanol‐partition coefficient (MLogP), and length‐to‐breadth ratio (L/Bw) were the variables responsible for the separation between active and inactive compounds against T. cruzi. Afterwards, the PCA, KNN, and DFA models built in this work were used to perform trypanocidal activity predictions for eight new megazol analog compounds. © 2005 Wiley Periodicals, Inc. Int J Quantum Chem, 2005

Biological spectra analysis: Linking biological activity profiles to molecular structure.

Establishing quantitative relationships between molecular structure and broad biological effects has been a longstanding challenge in science. Currently, no method exists for forecasting broad biological activity profiles of medicinal agents even within narrow boundaries of structurally similar molecules. Starting from the premise that biological activity results from the capacity of small organic molecules to modulate the activity of the proteome, we set out to investigate whether descriptor sets could be developed for measuring and quantifying this molecular property. Using a 1,567-compound database, we show that percent inhibition values, determined at single high drug concentration in a battery of in vitro assays representing a cross section of the proteome, provide precise molecular property descriptors that identify the structure of molecules. When broad biological activity of molecules is represented in spectra form, organic molecules can be sorted by quantifying differences between biological spectra. Unlike traditional structure-activity relationship methods, sorting of molecules by using biospectra comparisons does not require knowledge of a molecule's putative drug targets. To illustrate this finding, we selected as starting point the biological activity spectra of clotrimazole and tioconazole because their putative target, lanosterol demethylase (CYP51), was not included in the bioassay array. Spectra similarity obtained through profile similarity measurements and hierarchical clustering provided an unbiased means for establishing quantitative relationships between chemical structures and biological activity spectra. This methodology, which we have termed biological spectra analysis, provides the capability not only of sorting molecules on the basis of biospectra similarity but also of predicting simultaneous interactions of new molecules with multiple proteins.

A theoretical study on the analgesic activity of cannabinoid compounds

A set of 29 cannabinoid compounds with analgesic activity was studied by using the quantum chemical semi-empirical method AM1 and chemometric methods with the aim to correlate some calculated molecular properties (descriptors) of the compounds and their biological activity. The pattern recognition methods principal component analysis (PCA) and hierarchical clustering analysis (HCA) have been employed in order to investigate the possible relationships between the molecular descriptors and the analgesic activity. The PCA and HCA results showed that the descriptors charge density on substituent at position C 3 ( R 3), charge on atom C 1 ( Q 1), surface area ( A), logarithm of the partition coefficient (log P) and molecular refractivity (MR) are responsible for the classification among the active, moderately active and inactive compounds. These results allow us to rationally propose new cannabinoid compounds as potential candidates for synthesis and biological evaluation regarding the analgesic activity.

Global and local computational models for aqueous solubility prediction of drug-like molecules.

The aim of this study was to develop in silico protocols for the prediction of aqueous drug solubility. For this purpose, high quality solubility data of 85 drug-like compounds covering the total drug-like space as identified with the ChemGPS methodology were used. Two-dimensional molecular descriptors describing electron distribution, lipophilicity, flexibility, and size were calculated by Molconn-Z and Selma. Global minimum energy conformers were obtained by Monte Carlo simulations in MacroModel and three-dimensional descriptors of molecular surface area properties were calculated by Marea. PLS models were obtained by use of training and test sets. Both a global drug solubility model (R(2) = 0.80, RMSE(te) = 0.83) and subset specific models (after dividing the 85 compounds into acids, bases, ampholytes, and nonproteolytes) were generated. Furthermore, the final models were successful in predicting the solubility values of external test sets taken from the literature. The results showed that homologous series and subsets can be predicted with high accuracy from easily comprehensible models, whereas consensus modeling might be needed to predict the aqueous drug solubility of datasets with large structural diversity.

A semi-empirical study of biflavonoid compounds with biological activity against tuberculosis

Biflavonoids are a series of naturally occurring with a variety of biological activities. In this work the PM3 semi-empirical method was employed to calculate a set of molecular properties (variables or descriptors) of 28 biflavonoid compounds with inhibitory activity against Mycobacterium tuberculosis H37Rv ( Mtb). We have observed a correlation between the heat of formation (Hf), log of the octanol/water partition coefficient ( log P) or hydration energy (HE) and the antituberculosis activity. The active compounds present larger values for log P, Hf, and HE. These results suggest that it is possible, in principle, to select the most (or the least) promising molecules from a series of untested biflavonoid molecules, simply by comparing their Hf, log P, or HE.

A QSAR study of 8.O.4′-neolignans with antifungal activity

The PM3 semi-empirical method was employed to calculate a set of molecular properties (variables or descriptors) of 18 neolignans compounds with activities against Epidermophyton floccosum a most susceptible species of dermophytes. The correlation between biological activity and structural properties was obtained by using the multiple linear regression method. The model obtained showed not only statistical significance but also predictive ability. The significant molecular descriptors related to the compounds with antifungal activity were: hydration energy and the charge on C1′ carbon atom ( Q 1′). The model obtained was applied to a set of 10 new derived from neolignans, where five of them presented promising biological activities against E. floccosum.

Two Identical Twin Nitrogen Mustard Agents that Express Rapid Alkylation Activity at Physiological pH 7.4 and 37°C

Two nitrogen mustard (N-mustard ) agents were synthesized utilizing ethylene diamine and hexane diamine as the parent compounds. These N-mustard agents were solids at 25 o C and stable while stored dry at -10 o C. The two N-mustards assumed the configuration of identical twin drugs which when placed in aqueous solution were highly reactive. Both N-mustards were soluble in aqueous NaHCO3 buffer and expressed alkylation activity directed towards a nucleophilic primary amine target (4-chloroaniline) at blood pH 7.4 and 37 o C. Utilizing the fluorescent probe fluorescamine, which is highly specific for primary amines, the quantity of unreacted nucleophilic 4-chloroaniline remaining after a known time period was determined. This enabled the calculation of rate constants and the determination of rate equation for alkylation to be first-order for N,N,N',N'-tetrakis(2-chloroethyl)ethane-1,2-diamine and zero-order for N,N,N',N'- tetrakis(2-chloroethyl)hexane-1,6-diamine. Molecular property descriptors such as Log P, parachor, molar volume, molar refractivity, dipole, molecular volume & area, and polar surface area were calculated for comparison. Zero violations of the Rule of 5 indicates these two mustard agents will have good bioavailability and good bioactivity.

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.

Partitioning in binary-transformed chemical descriptor spaces.

Here we describe a statistically based partitioning method called median partitioning (MP), which involves the transformation of value distributions of molecular property descriptors into a binary classification scheme. The MP approach fundamentally differs from other partitioning approaches that involve dimension reduction of chemical spaces such as cell-based partitioning, since MP directly operates in original, albeit simplified, chemical space. Modified versions of the MP algorithm have been implemented and successfully applied in diversity selection, compound classification, and virtual screening. These findings have demonstrated that dimension reduction techniques, although elegant in their design, are not necessarily required for effective partitioning of molecular datasets. An attractive feature of statistical partitioning approaches such as decision tree methods or MP is their computational efficiency, which is becoming an important criterion for the analysis of compound databases containing millions of molecules.

Electronic van der Waals surface property descriptors and genetic algorithms for developing structure-activity correlations in olfactory databases.

A methodology to facilitate the intelligent design of new odorants (e.g., musks) with specialized properties has been developed as part of an ongoing research effort in machine learning. In a traditional framework, the introduction of a new odorant is a lengthy, costly, and laborious discovery, development, and testing process. We propose to streamline this process utilizing large existing olfactory databases available through the open scientific literature as input for a new structure/activity correlation methodology. The first step in this process is to characterize each molecule in the database by an appropriate set of descriptors. To accomplish this task, an enhanced version of Breneman's Transferable Atom Equivalent (TAE) descriptor methodology will be used to create a large set of electron density derived shape/property hybrid (PEST), wavelet coefficient (WCD), and TAE histogram descriptors. We have chosen these molecular property descriptors to represent the problem because they have been shown to contain pertinent shape and electronic properties of the molecule and correlate with key modes of intermolecular interactions. Traditional QSAR methodologies, which employ fragment based descriptors, have been shown to be effective for QSAR development within homologous sets of molecules but are less effective when applied to data sets containing a great deal of structural variation. In contrast to previous attempts at SAR, our use of shape-aware electron density based molecular property descriptors has removed many of the limitations brought about by the use of descriptors based on substructure fragments, molecular surface properties, or other whole molecule descriptors. Another reason for the mixed success of past QSAR efforts can be traced to the nature of the underlying modeling problem, which is often quite complex. To meet these challenges, a genetic algorithm for pattern recognition analysis has been developed that selects descriptors which create class separation in a plot of the two largest principal components of the data while simultaneously searching for features that increase clustering of the data.

The influence of electronic and steric effects in the structure–activity relationship (SAR) study of quinone compounds with biological activity against Trypanosoma cruzi

A set of 14 quinone compounds with anti-trypanocidal activity was studied by using the AM1 semi-empirical method in order to calculate atomic and molecular properties (variables or descriptors) to be correlated to the biological activity. Principal component analysis (PCA), hierarchical cluster analysis (HCA), stepwise discriminant analysis (SDA) and the Kth nearest neighbor (KNN) method were employed to obtain possible relationships between the calculated descriptors and the biological activity studied and to predict the anti-trypanocidal activity of new quinone compounds from a prediction set. The atomic and molecular descriptors responsible for the separation between the active and inactive compounds were: total energy ( E T), polarizability ( α) and the charge on the R 1 atom ( Q 4). These descriptors give information on the kind of interaction that can occur between the compounds and the biological receptor. The prediction study was done with a set of three new compounds by using the PCA, HCA, SDA and KNN methods and two of them were predicted as active against T. cruzi.

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.

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.

Classification of Biologically Active Compounds by Median Partitioning.

The median partitioning (MP) method was originally developed for the selection of diverse subsets from compound databases. Following this approach, property descriptors are used in subsequent steps to divide compounds into defined partitions from which representative molecules are selected. For descriptor analysis, MP was coupled to a genetic algorithm. MP subset selection does not depend on pairwise comparison of molecules and is therefore applicable to very large compound pools. Here the MP approach was evaluated for the classification of molecules according to biological activity. A total of 317 molecules belonging to 21 different activity classes were studied. MP compound classification calculations were carried out both in the presence and absence of 2000 randomly selected background molecules. The performance of MP was compared to cell-based partitioning and found to be at least comparable, with up to approximately 82% of active molecules occurring in pure partitions consisting only of molecules sharing the same activity. Different from cell-based methods, MP classification is based on direct and sequential contributions of molecular property descriptors. Our results suggest that MP in not only an effective method for the selection of diverse subsets but also for the classification of active compounds and searching for molecules with desired activity.

Predictive quantitative structure retention relationship models for ion-exchange chromatography

A database of probe molecules and their reported ion-exchange chromatographic data was collected from the literature, after which an extensive set of both traditional and novel molecular property descriptors were computed for each probe molecule. A genetic algorithm/partial least squares (GA/PLS) approach was then used on the data to create a predictive Quantitative Structure-Retention Relationship (QSRR) model of retention where a subset of the original data was used for training and the remainder of the data as a test set. The utility of this model was demonstrated by using it to predict the chromatographic behavior of compounds not included in the training set. The results presented in this paper demonstrate the utility of modern QSRR modeling to predict chromatographic behavior in ion-exchange systems.

A Structure-Activity Relationship (SAR) Study of Neolignan Compounds with Anti-schistosomiasis Activity

A set of eighteen neolignan derivative compounds with anti-schistosomiasis activity was studied by using the quantum mechanical semi-empirical method PM3 and other theoretical methods in order to calculate selected molecular properties (variables or descriptors) to be correlated to their biological activities. Exploratory data analysis (principal component analysis, PCA, and hierarchical cluster analysis, HCA), discriminant analysis (DA) and the Kth nearest neighbor (KNN) method were employed for obtaining possible relationships between the calculated descriptors and the biological activities studied and predicting the anti-schistosomiasis activity of new compounds from a test set. The molecular descriptors responsible for the separation between active and inactive compounds were: hydration energy (HE), molecular refractivity (MR) and charge on the C19 carbon atom (Q19). These descriptors give information on the kind of interaction that can occur between the compounds and their respective biological receptor. The prediction study was done with a new set of ten derivative compounds by using the PCA, HCA, DA and KNN methods and only five of them were predicted as active against schistosomiasis.

Accurate partitioning of compounds belonging to diverse activity classes.

Diverse sets of compounds were classified according to biological activity by use of a partitioning approach based on principal component analysis in conjunction with a genetic algorithm for molecular descriptor evaluation. Combinations of 236 molecular property and structural key descriptors were explored for their performance in classifying 317 molecules belonging to 21 distinct biological activity classes from various sources. Preferred descriptor combinations were further explored by complete factorial analysis. In these calculations, compounds having similar specific activity were predicted with greater than 80% accuracy.

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.