Structure-Activity Relationships for Non-Congeneric Structures. Application of Substructural Balance Method for Antiallergic Activity

Abstract

This paper presents a new research method of structure-activity relationships (SAR) based on the concept of substructural balance. By using antiallergic activity (PCA, rat, iv) of a non-congeneri set of 267 structures, the structural feature of active group is expressed in terms of substructural balanance. Each structure was expressed with 100 new substructures and the number of each substructure in a molecule was counted. The substructural balance was expressed as their ratio. Structures were classified into three groups based on their potencies (ED₅₀), active (44), median (33) and inactactive (190) group. Using two substructural ratios, 80.53% of inactive and 57.58% of median structures were excluded from those that were active. Common features of active structures were shown as a zone indicating the optimal ranges of two substructural ratios. Two substructural ratios were determined out of 4950 substructural ratios, all possible combinations of 100 substructures (₁₀₀C₂), by selecting the greatest discriminatory power of inactive from active structures. The substructures used in this work include: the number of bonds comprising of the longest conjugate system, the number of skeletal atoms and the numbers of electron-donor pairs at certain distances in the molecule.     

Application of infrared data analysis based on symbolic logic in automated structure elucidation by chemics

A computer program, IRASSL, is described for processing infrared (IR) data. The program, based on symbolic logic, is used to formulate complex relationships between substructures and their characteristic wavenumber regions. The program yields sets of substructures inferred from a correlation table prepared from accumulated IR data. The procedure is applied as an ancillary to the automated structure elucidation system CHEMICS. The introduction of the substructural information provided by IRASSL into CHEMICS as restrictions in the elucidation process produces a significant decrease in the number of candidate structures. Some examples of the application are given.     

Characterizing bitterness: identification of key structural features and development of a classification model

This work describes the first approach in the development of a comprehensive classification method for bitterness of small molecules. The data set comprises 649 bitter and 13 530 randomly selected molecules from the MDL Drug Data Repository (MDDR) which are analyzed by circular fingerprints (MOLPRINT 2D) and information-gain feature selection. The feature selection proposes substructural features which are statistically correlated to bitterness. Classification is performed on the selected features via a na?ve Bayes classifier. The substructural features upon which the classification is based are able to discriminate between bitter and random compounds, and thus we propose they are also functionally responsible for causing the bitter taste. Such substructures include various sugar moieties as well as highly branched carbon scaffolds. Cynaropicrine contains a number of the substructural features found to be statistically associated with bitterness and thus was correctly predicted to be bitter by our model. Alternatively, both promethazine and saccharin contain fewer of these substructural features, and thus the bitterness in these compounds was not identified. Two different classes of bitter compounds were identified, namely those which are larger and contain mainly oxygen and carbon and often sugar moieties, and those which are rather smaller and contain additional nitrogen and/or sulfur fragments. The classifier is able to predict 72.1% of the bitter compounds. Feature selection reduces the number of false-positives while also increasing the number of false negatives to 69.5% of bitter compounds correctly predicted. Overall, the method presented here presents both one of the largest databases of bitter compounds presently available as well as a relatively reliable classification method.     

Application of machine learning to improve the results of high-throughput docking against the HIV-1 protease

We have previously reported that the application of a Laplacian-modified naive Bayesian (NB) classifier may be used to improve the ranking of known inhibitors from a random database of compounds after High-Throughput Docking (HTD). The method relies upon the frequency of substructural features among the active and inactive compounds from 2D fingerprint information of the compounds. Here we present an investigation of the role of extended connectivity fingerprints in training the NB classifier against HTD studies on the HIV-1 protease using three docking programs: Glide, FlexX, and GOLD. The results show that the performance of the NB classifier is due to the presence of a large number of features common to the set of known active compounds rather than a single structural or substructural scaffold. We demonstrate that the Laplacian-modified naive Bayesian classifier trained with data from high-throughput docking is superior at identifying active compounds from a target database in comparison to conventional two-dimensional substructure search methods alone.     

Ligand-based virtual screening by novelty detection with self-organizing maps

We describe a novel method for ligand-based virtual screening, based on utilizing Self-Organizing Maps (SOM) as a novelty detection device. Novelty detection (or one-class classification) refers to the attempt of identifying patterns that do not belong to the space covered by a given data set. In ligand-based virtual screening, chemical structures perceived as novel lie outside the known activity space and can therefore be discarded from further investigation. In this context, the concept of "novel structure" refers to a compound, which is unlikely to share the activity of the query structures. Compounds not perceived as "novel" are suspected to share the activity of the query structures. Nowadays, various databases contain active structures but access to compounds which have been found to be inactive in a biological assay is limited. This work addresses this problem via novelty detection, which does not require proven inactive compounds. The structures are described by spatial autocorrelation functions weighted by atomic physicochemical properties. Different methods for selecting a subset of targets from a larger set are discussed. A comparison with similarity search based on Daylight fingerprints followed by data fusion is presented. The two methods complement each other to a large extent. In a retrospective screening of the WOMBAT database novelty detection with SOM gave enrichment factors between 105 and 462-an improvement over the similarity search based on Daylight fingerprints between 25% and 100%, when the 100 top ranked structures were considered. Novelty detection with SOM is applicable (1) to improve the retrieval of potentially active compounds also in concert with other virtual screening methods; (2) as a library design tool for discarding a large number of compounds, which are unlikely to possess a given biological activity; and (3) for selecting a small number of potentially active compounds from a large data set.     

ALADDIN: An integrated tool for computer-assisted molecular design and pharmacophore recognition from geometric,steric, and substructure searching of three-dimensional molecular structures

Summary ALADDIN is a computer program for the design or recognition of compounds that meet geometric, steric, and substructural criteria. ALADDIN searches a database of three-dimensional structures, marks atoms that meet substructural criteria, evaluates geometric criteria, and prepares a number of files that are input for molecular modification and coordinate generation as well as for molecular graphics. Properties calculated from the three-dimensional structure are described by either properties calculated from the molecule itself or from the molecule as compared to a reference molecule and associated surfaces. ALADDIN was used to design analogues to probe a bioactive conformation of a small molecule and a peptide, to test alternative superposition rules for receptor mapping of the D2 dopamine receptor, to recognize unexpected D2 dopamine agonist activity of existing compounds, and to design compounds to fit a binding site on a protein of known structure. We have found that series designed by ALADDIN show much more subtle variation in shape than do those designed by traditional methods and that compounds can be designed to be very close matches to the objective.     

Heterocyclization Strategy for Construction of Linear Conjugated Polymers: Efficient Metal‐Free Electrocatalysts for Oxygen Reduction

Exploring cost‐effective and efficient metal‐free electrocatalysts for the oxygen reduction reaction (ORR) is crucial for the development of energy conversion and storage technologies. Reported here is a novel heterocyclization strategy to construct efficient ORR catalysts based on linear conjugated polymers (LCPs), which are composed of N‐, S‐, or Se‐heterocycles. Among these polymers, the covalently linked pyridine and thiophene molecule ( P‐T ) with reduced graphene oxide (rGO) exhibits a remarkable half‐wave potential of 0.79 V (vs. RHE) and excellent electrochemical stability, which are among the highest values for metal‐free polymers as ORR catalysts. Density‐functional theory (DFT) calculations reveal that the molecule with a phenyl unit ( P‐Ph ) is catalytically inactive, and when a thiophene unit is introduced to replace the phenyl unit in the conjugated backbone it features highly efficient electrocatalytic active sites. More importantly, the well‐defined molecular structures and controllable active sites in the pyrolysis and metal‐free polymers highlight new opportunities for the catalytic metal‐free ORR.     

The Concise Connection Table: collected definitions with extensions for stereochemistry and saccharides.

The Concise Connection Table is a representation of molecular structures based on an implicit hierarchy of substructural components. This paper introduces a number of enhancements and extensions to the original definition, particularly relating to representations of stereochemistry and saccharides, and summarizes the overall principles of the CCT in one definitive presentation.     

New scoring functions for virtual screening from molecular dynamics simulations with a quantum-refined force-field (QRFF-MD). Application to cyclin-dependent kinase 2

A recently introduced new methodology based on ultrashort (50-100 ps) molecular dynamics simulations with a quantum-refined force-field (QRFF-MD) is here evaluated in its ability both to predict protein-ligand binding affinities and to discriminate active compounds from inactive ones. Physically based scoring functions are derived from this approach, and their performance is compared to that of several standard knowledge-based scoring functions. About 40 inhibitors of cyclin-dependent kinase 2 (CDK2) representing a broad chemical diversity were considered. The QRFF-MD method achieves a correlation coefficient, R(2), of 0.55, which is significantly better than that obtained by a number of traditional approaches in virtual screening but only slightly better than that obtained by consensus scoring (R(2) = 0.50). Compounds from the Available Chemical Directory, along with the known active compounds, were docked into the ATP binding site of CDK2 using the program Glide, and the 650 ligands from the top scored poses were considered for a QRFF-MD analysis. Combined with structural information extracted from the simulations, the QRFF-MD methodology results in similar enrichment of known actives compared to consensus scoring. Moreover, a new scoring function is introduced that combines a QRFF-MD based scoring function with consensus scoring, which results in substantial improvement on the enrichment profile.     

A new method for modelling spectator chemical groups in ab initio calculations: effective group potentials

 A new method for an increased numerical efficiency of ab initio calculations is proposed. It is based on the assumption that in most cases chemical properties of functional groups in molecules are mainly controlled by a few electrons. This statement allows one to distinguish between two classes of nuclei and electrons: active and inactive ones. The effective group potential (EGP) method presupposes that the effect of inactive electrons in a functional chemical group can be described by a pseudopotential, in the same way that core electrons are replaced by effective core potentials in atoms. It is shown that EGPs are able to predict chemical and structural features of the active part of a molecule and at a fraction of the ordinary computational cost. The preliminary results reported here concern the determination of EGPs for ammonia, the methyl radical and the cyclopendadienyl ligand, which represent different types of bonding. Received: 15 September 1999 / Accepted: 3 February 2000 / Published online: 2 May 2000     

Diketopyrrolopyrrole-based conjugated polymers as additives to optimize morphology for polymer solar cells

Novel random copolymers for optimizing the morphology of the active layer for high performance organic photovoltaic devices have been demonstrated. Three ternary random copolymers PTBDTDPPSi CN(3/7), PTBDTDPPSi CN(5/5), PTBDTDPPSi CN(7/3) were prepared by polymerization of electron-donating thienyl-substituted benzodithiophene(TBDT) with 2,5-bis[8-(1,1,3,3,5,5,5-heptamethyltrisiloxane-3-yl)octly]-pyrrolo[3,4-c]pyrrole-1,4-dione(DPPSi) and 2,5-dio[5-(5-cyano-5,5-dimethyl-pentyl)]-3,6-dithiophen-2-yl-pyrrolo[3,4-c]pyrrole-1,4-dione(DPPCN) of different ratios. The DPPCN block can well-tune the light absorption and molecular packing, while the DPPSi block is in favor of enhancing the charge mobility. And the formation of organic Si―O―Si networks is beneficial to stabilize the morphology of the active layer. These new copolymers have narrow bandgaps and broaden visible light absorption from 500 nm to 1000 nm. Careful balance of the contents of the trimethoxysilyl group and the cyano group can well-tune the surface energy and morphology of the copolymers. Incorporation of these novel copolymers as additives into the blend of poly(3-hexylthiophene)(P3HT) and [6,6]-phenyl-C60-butyric acid methyl ester(PC_(61)BM) is found to effectively broaden the light absorption, improve the compatibility and morphology of the active layer. As a result, some devices with certain ratios of these copolymers as additives achieve the enhanced efficiency compared with the device based on pristine P3HT:PC_(61)BM.     

Comparison of substructural epitopes in enzyme active sites using self-organizing maps

Summary This paper presents a new algorithm to compare substructural epitopes in protein binding cavities. Through the comparison of binding cavities accommodating well characterized ligands with cavities whose actual guests are yet unknown, it is possible to draw some conclusions on the required shape of a putative ligand likely to bind to the latter cavities. To detect functional relationships among proteins, their binding-site exposed physicochemical characteristics are described by assigning generic pseudocenters to the functional groups of the amino acids flanking the particular active site. The cavities are divided into small local regions of four pseudocenters having the shape of a pyramid with triangular basis. To find similar local regions, an emergent self-organizing map is used for clustering. Two local regions within the same cluster are similar and form the basis for the superpositioning of the corresponding cavities to score this match. First results show that the similarities between enzymes with the same EC number can be found correctly. Enzymes with different EC numbers are detected to have no common substructures. These results indicate the benefit of this method and motivate further studies.     

A versatile synthetic approach to peptidyl privileged structures using a "safety-catch" linker

Peptidyl privileged structures have been widely used by many groups to discover biologically active molecules. In this context, privileged substructures are used as "hydrophobic anchors", to which peptide functionality is appended to gain specificity. Utilization of this concept has led to the discovery of many different active compounds at a wide range of biological receptors. A synthetic approach to these compounds has been developed on a "safety-catch" linker that allows rapid preparation of large libraries of these molecules. Importantly, amide bond formation/cleavage through treatment with amines is the final step; it is a linker strategy that allows significant diversification to be easily incorporated, and it only requires the inclusion of an amide bond. In addition, chemistry has been developed that permits the urea moiety to be inserted at the N-terminus of the peptide, allowing the same set of amines (either privileged substructures or amino acid analogues) to be used at both the N- and C-termini of the molecule. To show the robustness of this approach, a small library of peptidyl privileged structures were synthesized, illustrating that large combinatorial libraries can be synthesized using these technologies.     

AADS--an automated active site identification, docking, and scoring protocol for protein targets based on physicochemical descriptors

We report here a robust automated active site detection, docking, and scoring (AADS) protocol for proteins with known structures. The active site finder identifies all cavities in a protein and scores them based on the physicochemical properties of functional groups lining the cavities in the protein. The accuracy realized on 620 proteins with sizes ranging from 100 to 600 amino acids with known drug active sites is 100% when the top ten cavity points are considered. These top ten cavity points identified are then submitted for an automated docking of an input ligand/candidate molecule. The docking protocol uses an all atom energy based Monte Carlo method. Eight low energy docked structures corresponding to different locations and orientations of the candidate molecule are stored at each cavity point giving 80 docked structures overall which are then ranked using an effective free energy function and top five structures are selected. The predicted structure and energetics of the complexes agree quite well with experiment when tested on a data set of 170 protein-ligand complexes with known structures and binding affinities. The AADS methodology is implemented on an 80 processor cluster and presented as a freely accessible, easy to use tool at http://www.scfbio-iitd.res.in/dock/ActiveSite_new.jsp .     

Rho GTPase activating proteins in cancer phenotypes

Rho proteins belong to the Ras superfamily of small GTPases and function as binary switches that shuttle between active and inactive states based on the nature of bound guanine nucleotide. Three sets of regulatory proteins, namely, guanine dissociation inhibitors, guanine exchange factors, and GTPase activating proteins (GAPs) control the balance between active and inactive Rho proteins. There are more than 70 RhoGAPs encoded in the human genome. The RhoGAP family is distinguished by the presence of the RhoGAP domain. However, the majority of RhoGAPs contain multiple additional domains. There are as many as eight domains in some of these proteins. The modular structure of GAPs is important for their interaction with other proteins. A significant number of RhoGAPs have been shown to be present in altered abundance in a variety of human cancers or cell lines. The ability of RhoGAPs to modulate Rho mediated signaling pathways may lend themselves as targets for small molecule therapeutic agents against cancer.     

Quantum-chemical foundations of the topological substructural molecular design

The topological substructural molecular design (TOPS-MODE) approach is formulated as a tight-binding quantum-chemical method. The approach is based on certain postulates that permit to express any molecular property as a function of the spectral moments of certain types of molecular and environment-dependent energies. We use several empirical potentials to account for these intrinsic and external molecular energies. We prove that any molecular property expressed in terms of a quantitative structure-property and structure-activity relationships (QSPR/QSAR) model developed by using the TOPS-MODE method can be expressed as a bond additivity function. In addition, such a property can also be expressed as a substructural cluster expansion function. The conditions for such bond contributions being transferable are also analyzed here. Several new statistical-mechanical electronic functions are introduced as well as a bond-bond thermal Green's function or a propagator accounting for the electronic hopping between pairs of bonds. All these new concepts are applied to the development and application of a new QSAR model for describing the toxicity of polyhalogenated-dibenzo-1,4-dioxins. The QSAR model obtained displays a significant robustness and predictability. It permits an easy structural interpretation of the structure-activity relationship in terms of bond additivity functions, which display some resemblances with other theoretical parameters obtained from first principle quantum-chemical methods.