Use of statistical and neural net approaches in predicting toxicity of chemicals

Symmetry and Combinatorial Concepts for Cyclopolyarenes, Nanotubes and 2D-Sheets: Enumerations, Isomers, Structures Spectra & Properties

This review article highlights recent developments in symmetry, combinatorics, topology, entropy, chirality, spectroscopy and thermochemistry pertinent to 2D and 1D nanomaterials such as circumscribed-cyclopolyarenes and their heterocyclic analogs, carbon and heteronanotubes and heteronano wires, as well as tessellations of cyclopolyarenes, for example, kekulenes, septulenes and octulenes. We establish that the generalization of Sheehan’s modification of Pólya’s theorem to all irreducible representations of point groups yields robust generating functions for the enumeration of chiral, achiral, position isomers, NMR, multiple quantum NMR and ESR hyperfine patterns. We also show distance, degree and graph entropy based topological measures combined with techniques for distance degree vector sequences, edge and vertex partitions of nanomaterials yield robust and powerful techniques for thermochemistry, bond energies and spectroscopic computations of these species. We have demonstrated the existence of isentropic tessellations of kekulenes which were further studied using combinatorial, topological and spectral techniques. The combinatorial generating functions obtained not only enumerate the chiral and achiral isomers but also aid in the machine construction of various spectroscopic and ESR hyperfine patterns of the nanomaterials that were considered in this review. Combinatorial and topological tools can become an integral part of robust machine learning techniques for rapid computation of the combinatorial library of isomers and their properties of nanomaterials. Future applications to metal organic frameworks and fullerene polymers are pointed out.

Symmetry, Combinatorics, Artificial Intelligence, Music and Spectroscopy

Symmetry forms the foundation of combinatorial theories and algorithms of enumeration such as Möbius inversion, Euler totient functions, and the celebrated Pólya’s theory of enumeration under the symmetric group action. As machine learning and artificial intelligence techniques play increasingly important roles in the machine perception of music to image processing that are central to many disciplines, combinatorics, graph theory, and symmetry act as powerful bridges to the developments of algorithms for such varied applications. In this review, we bring together the confluence of music theory and spectroscopy as two primary disciplines to outline several interconnections of combinatorial and symmetry techniques in the development of algorithms for machine generation of musical patterns of the east and west and a variety of spectroscopic signatures of molecules. Combinatorial techniques in conjunction with group theory can be harnessed to generate the musical scales, intensity patterns in ESR spectra, multiple quantum NMR spectra, nuclear spin statistics of both fermions and bosons, colorings of hyperplanes of hypercubes, enumeration of chiral isomers, and vibrational modes of complex systems including supergiant fullerenes, as exemplified by our work on the golden fullerene C150,000. Combinatorial techniques are shown to yield algorithms for the enumeration and construction of musical chords and scales called ragas in music theory, as we exemplify by the machine construction of ragas and machine perception of musical patterns. We also outline the applications of Hadamard matrices and magic squares in the development of algorithms for the generation of balanced-pitch chords. Machine perception of musical, spectroscopic, and symmetry patterns are considered.

Structure, electronic and optical properties of chalcopyrite-type nano-clusters XFeY2 (X=Cu, Ag, Au; Y=S, Se, Te): a density functional theory study

Iron-based chalcopyrite materials have diverse applications in solar cells, spintronic, thermoelectric devices, LEDs and medical sciences. In this report we have studied structure, electronic and optical properties of chalcopyrite-type nano-cluster XFeY2 (X=Cu, Ag, Au; Y=S, Se, Te) systematically by using Density Functional Theory (DFT). Our computed HOMO-LUMO energy gap of XFeY2 is in the range of 1.568–3.982 eV, which endorses its potential application in optoelectronic devices and solar cells. The result shows that chalcopyrite-type material AuFeS2 having a star-type structure with point group C2v and sextet spin multiplicity, is the most stable cluster with HOMO-LUMO energy gap of 3.982 eV. The optical properties viz. optical electronegativity, refractive index, dielectric constant, IR and Raman activity of these nano-clusters are also investigated. The result exhibits that HOMO-LUMO energy gap of XFeY2 along with optical electronegativity and vibrational frequency decreases from S to Se to Te, whereas refractive index and dielectric constant increases in the reverse order.

Biochemical and phylogenetic networks-II: X-trees and phylogenetic trees

The present study, which is a continuation of the previous paper, augments a recent work on the use of phylogenetic networks. We develop techniques to characterize the topology of various X-trees and binary trees of biological and phylogenetic interests. We have obtained the results for various k-level X-trees and phylogenetic networks with variants of Zagreb, Szeged, Padmakar-Ivan, Schultz and Atom Bond Connectivity topological indices.

Topological and Thermodynamic Entropy Measures for COVID-19 Pandemic through Graph Theory

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused the global pandemic, coronavirus disease-2019 (COVID-19) which has resulted in 60.4 million infections and 1.42 million deaths worldwide. Mathematical models as an integral part of artificial intelligence are designed for contact tracing, genetic network analysis for uncovering the biological evolution of the virus, understanding the underlying mechanisms of the observed disease dynamics, evaluating mitigation strategies, and predicting the COVID-19 pandemic dynamics. This paper describes mathematical techniques to exploit and understand the progression of the pandemic through a topological characterization of underlying graphs. We have obtained several topological indices for various graphs of biological interest such as pandemic trees, Cayley trees, Christmas trees, and the corona product of Christmas trees and paths. We have also obtained an analytical expression for the thermodynamic entropies of pandemic trees as a function of R0, the reproduction number, and the level of spread, using the nested wreath product groups. Our plots of entropy and logarithms of topological indices of pandemic trees accentuate the underlying severity of COVID-19 over the 1918 Spanish flu pandemic.

Comparison of Machine Learning Models on Performance of Single- and Dual-Type Electrochromic Devices

This study shows that the model fitting based on machine learning (ML) from experimental data can successfully predict the electrochromic characteristics of single- and dual-type flexible electrochromic devices (ECDs) by using tungsten trioxide (WO3) and WO3/vanadium pentoxide (V2O5), respectively. Seven different regression methods were used for experimental observations, which belong to single and dual ECDs where 80% percent was used as training data and the remaining was taken as testing data. Among the seven different regression methods, K-nearest neighbor (KNN) achieves the best results with higher coefficient of determination (R 2) score and lower root-mean-squared error (RMSE) for the bleaching state of ECDs. Furthermore, higher R 2 score and lower RMSE for the coloration state of ECDs were achieved with Gaussian process regressor. The robustness result of the ML modeling demonstrates the reliability of prediction outcomes. These results can be proposed as promising models for different energy-saving flexible electronic systems.

Cytotoxicity of Different Nano Composite Resins on Human Gingival and Periodontal Ligament Fibroblast Cell Lines: An In Vitro Study

The aim of this study is to determine the cytotoxicity of three different nano composite resins (CRs) on human gingival fibroblast (hGF) and periodontal ligament fibroblast (hPDLF) cell lines. These CRs selected were nanohybrid organic monomer-based Admira Fusion (AF), nanohybrid Bis-(acryloyloxymethyl) tricyclo [5.2.1.0.sup.2,6] decane-based Charisma Topaz (CT), and supra nano filled resin-based Estelite Quick Sigma (EQS). MTT assay was performed to assess the cytotoxicity of CRs at 24 h and one week. AF and EQS applied on hGF cells at 24 h and one week demonstrated similar cytotoxic outcomes. Cytotoxicity of CT on hGF cells at one week was higher than 24 h (p = 0.04). Cytotoxicity of CT on hGF cells was higher at 24 h (p = 0.002) and one week (p = 0.009) compared to control. All composites showed higher cytotoxicity on hPDLF cells at one week than the 24 h (AF; p = 0.02, CT; p = 0.02, EQS; p = 0.04). AF and EQS demonstrated lower cytotoxicity on hPDLF cells than the control group at 24 h (AF; p = 0.01, EQS; p = 0.001). CT was found more cytotoxic on hPDLF cells than the control (p = 0.01) and EQS group (p = 0.008) at one week. The cytotoxicity of CRs on hGF and hPDLF cells vary, according to the type of composites, cell types, and exposure time.

Quantum Molecular Dynamics, Topological, Group Theoretical and Graph Theoretical Studies of Protein-Protein Interactions

BACKGROUND

Protein-protein interactions (PPIs) are becoming increasingly important as PPIs form the basis of multiple aggregation-related diseases such as cancer, Creutzfeldt-Jakob, and Alzheimer's diseases. This mini-review presents hybrid quantum molecular dynamics, quantum chemical, topological, group theoretical, graph theoretical, and docking studies of PPIs. We also show how these theoretical studies facilitate the discovery of some PPI inhibitors of therapeutic importance.

OBJECTIVE

The objective of this review is to present hybrid quantum molecular dynamics, quantum chemical, topological, group theoretical, graph theoretical, and docking studies of PPIs. We also show how these theoretical studies enable the discovery of some PPI inhibitors of therapeutic importance.

METHODS

This article presents a detailed survey of hybrid quantum dynamics that combines classical and quantum MD for PPIs. The article also surveys various developments pertinent to topological, graph theoretical, group theoretical and docking studies of PPIs and highlight how the methods facilitate the discovery of some PPI inhibitors of therapeutic importance.

RESULTS

It is shown that it is important to include higher-level quantum chemical computations for accurate computations of free energies and electrostatics of PPIs and Drugs with PPIs, and thus techniques that combine classical MD tools with quantum MD are preferred choices. Topological, graph theoretical and group theoretical techniques are shown to be important in studying large network of PPIs comprised of over 100,000 proteins where quantum chemical and other techniques are not feasible. Hence, multiple techniques are needed for PPIs.

CONCLUSION

Drug discovery and our understanding of complex PPIs require multifaceted techniques that involve several disciplines such as quantum chemistry, topology, graph theory, knot theory and group theory, thus demonstrating a compelling need for a multi-disciplinary approach to the problem.

Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis

Toxicity prediction is very important to public health. Among its many applications, toxicity prediction is essential to reduce the cost and labor of a drug's preclinical and clinical trials, because a lot of drug evaluations (cellular, animal, and clinical) can be spared due to the predicted toxicity. In the era of Big Data and artificial intelligence, toxicity prediction can benefit from machine learning, which has been widely used in many fields such as natural language processing, speech recognition, image recognition, computational chemistry, and bioinformatics, with excellent performance. In this article, we review machine learning methods that have been applied to toxicity prediction, including deep learning, random forests, k-nearest neighbors, and support vector machines. We also discuss the input parameter to the machine learning algorithm, especially its shift from chemical structural description only to that combined with human transcriptome data analysis, which can greatly enhance prediction accuracy.

Predicting aquatic toxicities of benzene derivatives in multiple test species using local, global and interspecies QSTR modeling approaches

A flow diagram showing QSTR modeling strategy for aquatic toxicity prediction of benzene derivatives in multiple test species.

Machine learning methods in chemoinformatics

Machine learning algorithms are generally developed in computer science or adjacent disciplines and find their way into chemical modeling by a process of diffusion. Though particular machine learning methods are popular in chemoinformatics and quantitative structure-activity relationships (QSAR), many others exist in the technical literature. This discussion is methods-based and focused on some algorithms that chemoinformatics researchers frequently use. It makes no claim to be exhaustive. We concentrate on methods for supervised learning, predicting the unknown property values of a test set of instances, usually molecules, based on the known values for a training set. Particularly relevant approaches include Artificial Neural Networks, Random Forest, Support Vector Machine, k-Nearest Neighbors and naïve Bayes classifiers.

Predicting acute aquatic toxicity of structurally diverse chemicals in fish using artificial intelligence approaches

The research aims to develop global modeling tools capable of categorizing structurally diverse chemicals in various toxicity classes according to the EEC and European Community directives, and to predict their acute toxicity in fathead minnow using set of selected molecular descriptors. Accordingly, artificial intelligence approach based classification and regression models, such as probabilistic neural networks (PNN), generalized regression neural networks (GRNN), multilayer perceptron neural network (MLPN), radial basis function neural network (RBFN), support vector machines (SVM), gene expression programming (GEP), and decision tree (DT) were constructed using the experimental toxicity data. Diversity and non-linearity in the chemicals' data were tested using the Tanimoto similarity index and Brock-Dechert-Scheinkman statistics. Predictive and generalization abilities of various models constructed here were compared using several statistical parameters. PNN and GRNN models performed relatively better than MLPN, RBFN, SVM, GEP, and DT. Both in two and four category classifications, PNN yielded a considerably high accuracy of classification in training (95.85 percent and 90.07 percent) and validation data (91.30 percent and 86.96 percent), respectively. GRNN rendered a high correlation between the measured and model predicted -log LC50 values both for the training (0.929) and validation (0.910) data and low prediction errors (RMSE) of 0.52 and 0.49 for two sets. Efficiency of the selected PNN and GRNN models in predicting acute toxicity of new chemicals was adequately validated using external datasets of different fish species (fathead minnow, bluegill, trout, and guppy). The PNN and GRNN models showed good predictive and generalization abilities and can be used as tools for predicting toxicities of structurally diverse chemical compounds.

TOPOCHEMICAL MODELS FOR THE PREDICTION OF LIPOPHILICITY OF 1,3-DISUBSTITUTED PROPAN-2-ONE ANALOGS

In the present study, the relationship between the topochemical indices and log P values of 1,3-disubstituted propan-2-one analogs has been investigated. Three topochemical indices, Wiener's topochemical index — a distance-based topochemical descriptor, molecular connectivity topochemical index — an adjacency-based topochemical descriptor, and eccentric connectivity topochemical index — an adjacency-cum-distance-based topochemical descriptor, were used for the present investigation. The values of the Wiener's topochemical index, molecular connectivity topochemical index, and eccentric connectivity topochemical index were computed for each of the 45 analogs constituting the data set using an in-house computer program. The predicted log P values using leave-one-out (LOO) procedure exhibited a q2 of 0.72, 0.70, and 0.71 with reported log P values for Wiener's topochemical index, molecular connectivity topochemical index, and eccentric connectivity topochemical index, respectively. Separate models were developed using training set and log P of each analog in the independent test set was predicted using these models. The correlation of predicted log P values with the reported values, for independent test set, were in good agreement with those predicted using LOO procedure.

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.

Prediction of carcinogenicity for diverse chemicals based on substructure grouping and SVM modeling

The Carcinogenicity Reliability Database (CRDB) was constructed by collecting experimental carcinogenicity data on about 1,500 chemicals from six sources, including IARC, and NTP databases, and then by ranking their reliabilities into six unified categories. A wide variety of 911 organic chemicals were selected from the database for QSAR modeling, and 1,504 kinds of different molecular descriptors were calculated, based on their 3D molecular structures as modeled by the Dragon software. Positive (carcinogenic) and negative (non-carcinogenic) chemicals containing various substructures were counted using atom and functional group count descriptors, and the statistical significance of ratios of positives to negatives was tested for those substructures. Very few were judged to be strongly related to carcinogenicity, among substructures known to be responsible for carcinogens as revealed from biomedical studies. In order to develop QSAR models for the prediction of the carcinogenicities of a wide variety of chemicals with a satisfactory performance level, the relationship between the carcinogenicity data with improved reliability and a subset of significant descriptors selected from 1,504 Dragon descriptors was analyzed with a support vector machine (SVM) method: the classification function (SVC) for weighted data in LIBSVM program was used to classify chemicals into two carcinogenic categories (positive or negative), where weights were set depending on the reliabilities of the carcinogenicity data. The quality and stability of the models presented were tested by performing a dual cross-validation procedure. A single SVM model as the first step was developed for all the 911 chemicals using 250 selected descriptors, achieving an overall accuracy level, i.e., positive and negative correct estimate, of about 70%. In order to improve the accuracy of the final model, the 911 chemicals were classified into 20 mutually overlapping subgroups according to contained substructures, a specific SVM model was optimized for each subgroup, and the predicted carcinogenicities of the 911 chemicals were determined by the majorities of the outputs of the corresponding SVM models. The model developed on the basis of grouping of chemicals into 20 substructures predicts the carcinogenicities of a wide variety of chemicals with a satisfactory overall accuracy of approximately 80%.

QSAR with quantum topological molecular similarity indices: toxicity of aromatic aldehydes to Tetrahymena pyriformis

Extensive production and utilization of aromatic aldehydes and their derivatives without proper certification is alarming with regard to environmental safety. This concern motivated our construction of predictive quantitative structure-activity relationship (QSAR) models for the toxicity of aldehydes to the ecologically important species Tetrahymena pyriformis. Quantum topological molecular similarity (QTMS) descriptors, along with the lipid-water partition coefficient (log K(o/w)), were used as predictor variables. The QTMS descriptors were calculated at different levels of theory including AM1, HF/3-21G(d), HF/6-31G(d), B3LYP/6-31 + G(d,p), B3LYP/6-311 + G(2d,p) and MP2/6-311+G(2d,p). The data set of 77 aromatic aldehydes was divided into a training set (n = 58) and a test (n = 19) set, and 58 models were developed using partial least squares (PLS) and genetic partial least squares (G/PLS). We evaluated the overall predictive capacity of the models based on leave-one-out predictions for the training set compounds and model derived predictions for the test set compounds. For both PLS and G/PLS, the models built at the HF/6-31G(d) level show better predictivity (based on overall prediction) than the models developed at any of the other five levels. Further validation was also performed utilizing (process and model) randomization tests. We show that improved predictive QSAR models for aldehydic toxicity to Tetrahymena pyriformis can be generated using QTMS descriptors along with log K(o/w).

Comparative study of machine-learning and chemometric tools for analysis of in-vivo high-throughput screening data

High-throughput screening (HTS) has become a central tool of many pharmaceutical and crop-protection discovery operations. If HTS screening is carried out at the level of the intact organism, as is commonly done in crop protection, this strategy has the potential of uncovering a completely new mechanism of actions. The challenge in running a cost-effective HTS operation is to identify ways in which to improve the overall success rate in discovering new biologically active compounds. To this end, we describe our efforts directed at making full use of the data stream arising from HTS. This paper describes a comparative study in which several machine learning and chemometric methodologies were used to develop classifiers on the same data sets derived from in vivo HTS campaigns and their predictive performances compared in terms of false negative and false positive error profiles.

Network models in drug discovery and regenerative medicine

Network motifs and modelling paradigms are attracting increasing attention as modelling tools in drug design and development, and in regenerative medicine. There is a gradual but inexorable convergence between these hitherto disparate disciplines. This review summarizes some very recent work in these areas, leading to an understanding of the complementary roles networks play and factors driving this convergence: network paradigms can be excellent ways of modelling and understanding drug molecules and their action, an understanding of the robustness and vulnerabilities of biological targets may improve the efficacy of drug design and discovery, drug design has an increasingly large role to play in directing stem cell properties, stem cell regulatory networks can be modelled in useful ways using network models at a reasonable level of scale, and the network tools of drug design are also very useful for the design of biomaterials used in regenerative medicine.

QSTR with extended topochemical atom (ETA) indices. VI. Acute toxicity of benzene derivatives to tadpoles (Rana japonica)

structure-toxicity relationship (QSTR) studies have proved to be a valuable approach in research on the toxicity of organic chemicals for ranking chemical substances with respect to their potential hazardous effects on living systems. With this background, we have modeled here the acute lethal toxicity of 51 benzene derivatives with recently introduced extended topochemical atom (ETA) indices [Roy and Ghosh, Internet Electron J Mol Des 2:599-620 (2003)]. We also compared the ETA relations with non-ETA models derived from different topological indices (Wiener W, Balaban J, flexibility index, Hosoya Z, Zagreb, molecular connectivity indices, E-state indices and kappa shape indices) and physicochemical parameters (AlogP98, MolRef,H_bond_donor and H_bond_acceptor). Genetic function approximation (GFA) and factor analysis (FA) were used as the data-preprocessing steps for the development of final multiple linear regression (MLR) equations. Principal-component regression analysis (PCRA) was also used to extract the total information from the ETA/non-ETA/combined matrices. All the models developed were cross-validated using leave-one-out (LOO) and leave-many-out techniques. The summary of the statistics of the best models is as follows: (1) FA-MLR: ETA model- Q 2 (LOO)=0.852, R 2=0.894; non-ETA model- Q 2=0.782, R 2=0.835; ETA + non-ETA model-Q 2 =0.815, R 2=0.859. (2) GFA-MLR: ETA model-Q 2 =0.847, R 2=0.915; non-ETA model-Q 2 =0.863, R 2=0.898; ETA + non-ETA model-Q 2 =0.859, R 2=0.893. 3. PCRA: ETA model-Q 2 =0.864, R 2=0.901; non-ETA model- Q 2=0.866, R 2=0.922; ETA + non-ETA model-Q 2=0.846, R 2=0.890. The statistical quality of the ETA models is comparable to that of non-ETA models. Again, use of non-ETA descriptors in addition to ETA descriptors does not increase the statistical acceptance of the relations significantly. The predictive potential of these models was better than that of the previously reported models using physicochemical parameters [Huang et al., Chemosphere 53:963-970 (2003)]. The relations from ETA descriptors suggest a parabolic dependence of the toxicity on molecular size. Furthermore, the toxicity increases with functionality contribution of chloro substituent and decreases with those of methoxy, hydroxy, carboxy and amino groups. This study suggests that ETA parameters are sufficiently rich in chemical information to encode the structural features that contribute significantly to the acute toxicity of benzene derivatives to Rana japonica.

Metabolic stability for drug discovery and development: pharmacokinetic and biochemical challenges

Metabolic stability refers to the susceptibility of compounds to biotransformation in the context of selecting and/or designing drugs with favourable pharmacokinetic properties. Metabolic stability results are usually reported as measures of intrinsic clearance, from which secondary pharmacokinetic parameters such as bioavailability and half-life can be calculated when other data on volume of distribution and fraction absorbed are available. Since these parameters are very important in defining the pharmacological and toxicological profile of drugs as well as patient compliance, the pharmaceutical industry has a particular interest in optimising for metabolic stability during the drug discovery and development process. In the early phases of drug discovery, new chemical entities cannot be administered to humans; hence, predictions of these properties have to be made from in vivo animal, in vitro cellular/subcellular and computational systems. The utility of these systems to define the metabolic stability of compounds that is predictive of the human situation will be reviewed here. The timing of performing the studies in the discovery process and the impact of recent advances in research on drug absorption, distribution, metabolism and excretion (ADME) will be evaluated with respect to the scope and depth of metabolic stability issues. Quantitative prediction of in vivo clearance from in vitro metabolism data has, for many compounds, been shown to be poor in retrospective studies. One explanation for this may be that there are components used in the equations for scaling that are missing or uncertain and should be an area of more research. For example, as a result of increased biochemical understanding of drug metabolism, old assumptions (e.g. that the liver is the principal site of first-pass metabolism) need revision and new knowledge (e.g. the relationship between transporters and drug metabolising enzymes) needs to be incorporated into in vitro-in vivo correlation models. With ADME parameters increasingly being determined on automated platforms, instead of using results from high throughput screening (HTS) campaigns as simple go/no-go filters, the time saved and the many compounds analysed using the robots should be invested in careful processing of the data. A logical step would be to investigate the potential to construct computational models to understand the factors governing metabolic stability. A rational approach to the use of HTS assays should aim to screen for many properties (e.g. physicochemical parameters, absorption, metabolism, protein binding, pharmacokinetics in animals and pharmacology) in an integrated manner rather than screen against one property on many compounds, since it is likely that the final drug will represent a global average of these properties.

QSARs for chemical mutagens from structure: ridge regression fitting and diagnostics

QSAR models have been developed for a diverse set of mutagens using computed molecular descriptors. Such models can be used in predicting mutagenicity from structure. All common methods-regression, neural nets, k-nearest neighbors-are 'linear smoothers'-weighted averages of the activities in the calibration data with weights dependent on the descriptors. While they have been studied extensively, a vital but overlooked area is 'case diagnostics', pointers to compounds that are poorly fitted, or are unusually influential in fitting the model. This is particularly true where the measured activity is binary-present or absent. We illustrate the use of numeric and graphic diagnostics, particularly that of the FF plot, with a data set with 508 compounds and 307 structural descriptors used to predict mutagenicity.

The air-liquid interface of benzene, toluene, m-xylene, and mesitylene: a sum frequency, Raman, and infrared spectroscopic study

The air-liquid interface and the liquid-phase of benzene, toluene, 1,3-dimethylbenzene, and 1,3,5-trimethylbenzene are studied using broad bandwidth sum frequency generation spectroscopy, Raman and infrared spectroscopy. A vibrationally resonant sum frequency response is observed from these surfaces in spite of the small hyperpolarizabilities, in particular, the zero and near-zero hyperpolarizabilities of benzene and 1,3,5-trimethylbenzene. The orientation of the aromatic rings of these compounds at their air-liquid interfaces is tilted relative to the surface plane. Thus, on average, the plane of the aromatic ring does not lie in the interfacial plane. Comparison of the square root of the sum frequency intensity to that of the Raman multiplied bythe infrared intensity provides additional information about the molecular environment at their respective air-liquid interface.

QSAR study on narcotic mechanism of action and toxicity: a molecular connectivity approach to Vibrio fischeri toxicity testing

Quantitative structure-activity relationships (QSARs) have been established based on narcotic mechanism of action and toxicity data to Vibrio fischeri using molecular connectivity indices. The results obtained suggest that both, the degree of branching and electronic characteristic of the compounds have dominant role in the exhibition of toxicity. The information obtained in the present study will be useful in designing more potent compounds.

QSAR study on toxicity to aqueous organisms using the PI index

We have attempted to develop quantitative structure-toxicity relationships (QSTRs) to predict hydrophobicity (logP) as well as toxicity (pEC50 microm) of benzene derivatives using recently introduced Padmakar-Ivan (PI) index. The results have shown that both logP as well as pEC50 of benzene derivatives can be modelled excellently in multiparametric models in that the PI index and some indicator parameters are involved. The predictive ability of the models is discussed on the basis of the cross-validation method. The superiority of the PI index over several other topological indices is critically examined.

The impact of informatics and computational chemistry on synthesis and screening

High-throughput synthesis and screening technologies have enhanced the impact of computational chemistry on the drug discovery process. From the design of targeted, drug-like libraries to 'virtual' optimization of potency, selectivity and ADME/Tox properties, computational chemists are able to efficiently manage costly resources and dramatically shorten drug discovery cycle times. This review will describe some of the successful strategies and applications of state-of-the-art algorithms to enhance drug discovery, as well as key points in the drug discovery process where computational methods can have, and have had, greatest impact.

QSAR with few compounds and many features

Fitting quantitative structure-activity relationships (QSAR) requires different statistical methodologies and, to some degree, philosophies depending on the "shape" of the data matrix. When few features are used and there are many compounds, it is a reasonable expectation that good feature subset selection may be made and that nonlinearities and nonadditivities can be detected and diagnosed. Where there are many features and few compounds, this is unrealistic. Methods such as ridge regression RR, PLS, and principal component regression PCR, which abjure feature selection and rely on linearity may provide good predictions and fair understanding. We report a development of ridge regression for the underdetermined case by using generalized cross-validation to choose the ridge constant and perform F-tests for additional information. Conventional regression diagnostics can be used in followup to identify nonlinearities and other departures from model. We illustrate the approach with QSAR models of four data sets using calculated molecular descriptors.