Rapid evaluation of synthetic and molecular complexity for in silico chemistry

Methods that rapidly evaluate molecular complexity and synthetic feasibility are becoming increasingly important for in silico chemistry. We propose a new metric based on relative atomic electronegativities and bond parameters that evaluate both synthetic and molecular complexity (SMCM) starting from chemical structures. Against molecular weight, SMCM has the lowest fraction of adjusted variance (R2=0.535) on a series of 261,048 diverse compounds, when compared to the complexity metric of Baron and Chanon (R2=0.777; J. Chem. Inf. Comput. Sci. 2001, 41, 269-272) and Rücker (R2=0.895 for log complexity values; J. Chem. Inf. Comput. Sci. 2004, 44, 378-386), respectively. These metrics are in general agreement when the metabolic synthesis of cholesterol from S-3-hydroxy-3-methyl-glutaryl coenzyme A is monitored, indicating that SMCM can be useful in discerning increases in complexity. Because the presence of substructure patterns can be directly incorporated into this scheme, SMCM is relatively straightforward and can be easily tailored to rapidly evaluate virtual (combinatorial) libraries and high throughput screening hits.     

Information content in organic molecules: quantification and statistical structure via Brownian processing

Information and organic molecules were the subject of two previous works from this lab (Graham and Schacht, J. Chem. Inf. Comput. Sci. 2000, 40, 187; Graham, J. Chem. Inf. Computer Sci. 2002, 42, 215). We delve further in this paper by examining organic structure graphs as objects of Brownian information processing. In so doing, tools are introduced which quantify and correlate molecular information to several orders. When the results are combined with energy data, an enhanced informatic view of covalent bond networks is obtained. The information properties of select molecules and libraries are illustrated. Notably, Brownian processing accommodates all possible compounds and libraries, not just ones registered in chemical databases. This approach establishes important features of the statistical structure underlying carbon chemistry.     

Spectral moments of phenylenes

In a series of publications Estrada (Estrada, E. J. Chem. Inf. Comput. Sci. 1996, 36, 844-849; 1997 37, 320-328; 1998, 38, 23-27) employed spectral moments of line graphs in QSPR and QSAR relationship studies of various classes of compounds. A recent paper (Markovi?, S.; Gutman, I. J. Chem. Inf Comput. Sci. 1999, 39, 289-293) reported that in QSPR and QSAR investigations of benzenoid hydrocarbons based on linear combination of spectral moments, it made no difference whether one used spectral moments of the molecular graph or those of the line graph. In the present work spectral moments of molecular graphs (Mk) and line graphs (muk) of phenylenes are considered. The first few Mk's and muk'S of phenylenes are dependent on identical structural parameters. It is proved that the two sets of moments of phenylenes are linearly dependent. It is also shown that in the case of the heat of formation of phenylenes there is no advantage in using lower spectral moments of line graphs instead of lower spectral moments of molecular graphs. In this way the redundancy observed in the case of benzenoid hydrocarbons is also shown to exist in the class of phenylenes.     

Comparison of Reliability of log P Values Calculated from a Group Contribution Approach and from the Autocorrelation Method

Abstract

The values of log P (n-octanol/water partition coefficient) for 1449 chemicals were calculated from the fragmental/regression model of Klopman and co-workers (J. Chem. Inf. Comput. Sci. 1994, 34, 752-781) and from the autocorrelation/backpropagation neural network model implemented in AUTOLOGP_TM (Version 4.0). Both models provided good simulation results but the superiority of the latter was demonstrated.     

Feature extraction using molecular planes for fuzzy relational clustering of a flexible dopamine reuptake inhibitor

Six rigid-body parameters (Shift, Slide, Rise, Tilt, Roll, Twist) are commonly used to describe the relative displacement and orientation of successive base pairs in a nucleic acid structure. The present work adapts this approach to describe the relative displacement and orientation of any two planes in an arbitrary molecule-specifically, planes which contain important pharmacophore elements. Relevant code from the 3DNA software package (Nucleic Acids Res. 2003, 31, 5108-5121) was generalized to treat molecular fragments other than DNA bases as input for the calculation of the corresponding rigid-body (or "planes") parameters. These parameters were used to construct feature vectors for a fuzzy relational clustering study of over 700 conformations of a flexible analogue of the dopamine reuptake inhibitor, GBR 12909. Several cluster validity measures were used to determine the optimal number of clusters. Translational (Shift, Slide, Rise) rather than rotational (Tilt, Roll, Twist) features dominate clustering based on planes that are relatively far apart, whereas both types of features are important to clustering when the pair of planes are close by. This approach was able to classify the data set of molecular conformations into groups and to identify representative conformers for use as template conformers in future Comparative Molecular Field Analysis studies of GBR 12909 analogues. The advantage of using the planes parameters, rather than the combination of atomic coordinates and angles between molecular planes used in our previous fuzzy relational clustering of the same data set (J. Chem. Inf. Model. 2005, 45, 610-623), is that the present clustering results are independent of molecular superposition and the technique is able to identify clusters in the molecule considered as a whole. This approach is easily generalizable to any two planes in any molecule.     

DDLm: A New Dictionary Definition Language

A previous paper [Spadaccini and Hall J. Chem. Inf. Model. doi:10.1021/ci300074v] details extensions to the STAR File [Hall J. Chem. Inf. Comput. Sci.1991, 31, 326-333] syntax that will improve the exchange and archiving of electronic data. This paper describes a dictionary definition language (DDLm) for defining STAR File data items in a domain dictionary. A dictionary that defines the ontology and vocabulary of a discipline is built with DDLm, which is itself implemented in STAR, and is extensible and machine parsable. The DDLm is semantically rich and highly specific; provides strong data typing, data enumerations, and ranges; enables relationship keys between data items; and uses imbedded methods written in dREL [Spadaccini et al. J. Chem. Inf. Model. doi:10.1021/ci300076w] for data validation and evaluation and for refining data definitions. It promotes the modular definition of the discipline ontology and reuse through the ability to import definitions from other local and remote dictionaries, thus encouraging the sharing of data dictionaries within and across domains.     

A high energy barrier to charge recombination in ionized water vapor

A quantitative kinetic theory has been developed to explain the results of experimental observation of the abnormally intense clustering of water molecules in the vapor phase in an ionizing radiation field at a moderate dose rate. The recombination is impeded because of the hydration of ion pairs and the formation of an abnormally high energy barrier (∼100 k _B T) in molecular clusters. The buildup of the clusters of water molecules stabilized in the electric field of ion pairs results in a dramatic enhancement of the effect of ionizing radiation on the electric properties of the vapor. The values of the coefficients to the rate equation of ionization-recombination equilibrium were calculated on the molecular level by computer simulation of the hydration of the H₃O⁺(H₂O) _n OH⁻ ion pair using the detailed model of intermolecular interactions.     

Computer-assisted structure elucidation of organic compounds III: Automatic fragment generation from13C-NMR spectra

The interpretation of carbon-NMR spectra is mainly based on the comparison with suitable reference data taken from literature. The whole information contents of¹³C-NMR spectra cannot be utilized by manual interpretation. Therefore a network of interactive computer programs has been developed, which simulates the strategy of the spectroscopist in generating structural fragments from the spectral data. The most important knowledge source for this process is a carbon-NMR data base containing some 17,500 spectra. Structural fragments are generated automatically from this data file and assembled by a model builder to complete chemical structures using constraints derived from the spectral data. A comparison of the experimental carbon-NMR spectrum with the estimated ones allows the generation of a sorted hitlist.For part II see: H. Kalchhauser, W. Robien,J. Chem. Inf. Comput. Sci. 1985,25, 103.     

The importance of molecular complexity in the design of screening libraries

The one-dimensional model of Hann et al. (J Chem Inf Comput Sci 41(3):856–864) has been extended to include reverse binding and wrap-around interaction modes between the protein and ligand to explore the complete combinatorial matrix of molecular recognition. The cumulative distribution function of the Maxwell–Boltzmann distribution has been used to calculate the probability of measuring the sensitivity of the interactions as the asymptotic limits of the distribution better describe the behavior of the interactions under experimental conditions. Based on our model, we hypothesized that molecules of lower complexity are preferred for target based screening campaigns, while augmenting such a library with moieties of moderate complexities maybe better suited for phenotypic screens. The validity of the hypothesis has been assessed via the analysis of the hit rate profiles for four ChemBL datasets for enzymatic and phenotypic screens.     

Modeling boiling points of cycloalkanes by means of iterated line graph sequences

A class of models for predicting boiling points of cycloalkanes is put forward, based on iterated line graphs L(i), i = 1, 2,., of the molecular graph G = L(0). Let m(i) be the number of edges of L(i), i = 0, 1, 2,. The models analyzed are of the form a(0)m(i)()(0) + a(1)m(i)(1) + a(2)m(i)(2) +. + a(k)m(ik) + b. Our optimal QSPR formulas contain m(0), m(1), m(2), m(3), and/or m(4) but never m(5) and m(6). Their precision is as good as or better than the approximations recently reported by Rücker and Rücker (J. Chem. Inf. Comput. Sci. 1999, 39, 788-802).