Development of a Reverse Phase HPLC Retention Index Model for Nontargeted Metabolomics Using Synthetic Compounds.

The MolFind application has been developed as a nontargeted metabolomics chemometric tool to facilitate structure identification when HPLC biofluids analysis reveals a feature of interest. Here synthetic compounds are selected and measured to form the basis of a new, more accurate, HPLC retention index model for use with MolFind. We show that relatively inexpensive synthetic screening compounds with simple structures can be used to develop an artificial neural network model that is successful in making quality predictions for human metabolites.

A Model of Information Carried over a Neuron Soma.

A series of cellular automata models of amino acid side chains on a neuron soma membrane have been created to simulate their hydropathic influences on adjacent water molecules. The presence of pathways, referred to as water wires, is identified. These pathways are invoked as passage ways across a neuron soma of proton hopping carrying the information from dendrites to the axon hillock.

Optimizing artificial neural network models for metabolomics and systems biology: an example using HPLC retention index data.

Background: Artificial Neural Networks (ANN) are extensively used to model 'omics' data. Different modeling methodologies and combinations of adjustable parameters influence model performance and complicate model optimization.

Methodology: We evaluated optimization of four ANN modeling parameters (learning rate annealing, stopping criteria, data split method, network architecture) using retention index (RI) data for 390 compounds.

Development of a two-step indirect method for modeling Ecom50.

A novel approach is developed for modeling situations in which the modeled property is an algebraically transformed version of the original experimental data. In many cases such a transformation results in a data set with a significantly smaller data range. Here we explore the effects of range-of-data on modeling statistics.

The creation of proton hopping from a drug-receptor encounter.

We extend recent modeling studies of proton hopping, used to describe the functioning of membrane channels and axon nerve conduction, to offer an explanation of the initiation of the nerve impulse at an effector-ligand encounter. This encounter is proposed to create a hydronium ion in the vicinity of the effector and ligand, which leads to a continuous flow of protons, called proton hopping, through water adjacent to this encounter. This proton hopping is proposed to be the message carried from the encounter to the axon of a particular nerve system associated with that particular effector-ligand system.

A cellular automata model of proton hopping down a channel.

Proton hopping is the process where a H-atom on a hydronium ion forms a H-bond with the O-atom of a neighboring H(2)O molecule. There is then an exchange of bonding forces when that covalent bond of the H-atom in the hydronium ion changes to a H-bond, and the previous H-bond changes to a covalent bond with the neighboring O-atom. The neighboring molecule now becomes a hydronium (H(3)O(+)) ion.

Development of structure information from molecular topology for modeling chemical and biological properties: a tribute to the creativity of Lemont Burwell Kier on his 80th Birthday.

This review is a salute to Monty Kier's creativity. Emphasis is placed on creative aspects in the development of the representation of molecular topological structure information and the resultant formalisms: molecular connectivity and electrotopological state (E-State). Less attention is given to detailed analysis of individual papers and the generally well known books and book chapters.

Development of Ecom₅₀ and retention index models for nontargeted metabolomics: identification of 1,3-dicyclohexylurea in human serum by HPLC/mass spectrometry.

The goal of many metabolomic studies is to identify the molecular structure of endogenous molecules that are differentially expressed among sampled or treatment groups. The identified compounds can then be used to gain an understanding of disease mechanisms. Unfortunately, despite recent advances in a variety of analytical techniques, small molecule (<1000 Da) identification remains difficult.

Database searching for structural identification of metabolites in complex biofluids for mass spectrometry-based metabonomics.

MS and HPLC are commonly used for compound characterization and obtaining structural information; in the field of metabonomics, these two analytical techniques are often combined to characterize unknown endogenous or exogenous metabolites present in complex biological samples. Since the structures of a majority of these metabolites are not actually identified, the result of most metabonomic studies is a list of m/z values and retention times. However, without knowing actual structures, the biological significance of these 'features' cannot be determined.

CE50: quantifying collision induced dissociation energy for small molecule characterization and identification.

Survival yield analysis is routinely used in mass spectroscopy as a tool for assessing precursor ion stability and internal energy. Because ion internal energy and decomposition reaction rates are dependent on chemical structure, we reasoned that survival yield curves should be compound-specific and therefore useful for chemical identification. In this study, a quantitative approach for analyzing the correlation between survival yield and collision energy was developed and validated.

Prediction of HPLC retention index using artificial neural networks and IGroup E-state indices.

A back-propagation artificial neural network (ANN) was used to create a 10-fold leave-10%-out cross-validated ensemble model of high performance liquid chromatography retention index (HPLC-RI) for a data set of 498 diverse druglike compounds. A 10-fold multiple linear regression (MLR) ensemble model of the same data was developed for comparison. Molecular structure was described using IGroup E-state indices, a novel set of structure-information representation (SIR) descriptors, along with molecular connectivity chi and kappa indices and other SIR descriptors previously reported.

Modeling dipeptides as ACE inhibitors and bitter-tasting compounds by means of E-state structure-information representation.

Topological Structure-Information Representation (SIR) serves as the basis for QSAR model development on two data sets of dipeptides. Data sets of both bitter-taste (48 compounds) and angiotensin-converting-enzyme (ACE) inhibition (58 compounds) were analyzed by means of multiple linear-regression methods to produce QSAR models that relate structure to property. For the bitter-taste data set, two variables describe the data well, both being whole-molecule descriptors: (1)chi(v) (molecular connectivity first-order valence index) and SHBa (sum of E-State indices for H-bond acceptors) yield r(2)=0.

The prediction of ADMET properties using structure information representations.

The electrotopological state and molecular connectivity indices are defined as a system for molecular-structure description, using the term Structure-Information Representation. This system is built on the depiction of a molecule as a network composed of atoms of varying valence electron counts that constitute the valence state, bonded in discrete patterns constituting an electrotopological state. The system is employed in the structure-activity analysis of two sets of ADMET data.

Prediction of aqueous solubility based on large datasets using several QSPR models utilizing topological structure representation.

Several QSPR models were developed for predicting intrinsic aqueous solubility, S(o). A data set of 5,964 neutral compounds was sub-divided into two classes, aromatic and non-aromatic compounds. Three models were created with different methods on both data sets: two regression models (multiple linear regression and partial least squares) and an artificial neural network model.

A structure-information approach to the prediction of biological activities and properties.

The structure-information approach to quantitative biological modeling and prediction is presented in contrast to the mechanism-based approach. Basic structure information is developed from the chemical graph (connection table). The development, beginning with information explicit in the connection table (element identity and skeletal connections), leads to significant structure information useful for establishing sound models of a wide range of properties of interest in drug design.

Bioisosterism: quantitation of structure and property effects.

The powerful concept of bioisosterism is presented as a method for selecting molecular groups for drug design and lead-compound development. Three group-structure characteristics are described for this purpose. The E-State value for an attached atom is used as a measure of electrotopological group impact.

QSAR modeling of human serum protein binding with several modeling techniques utilizing structure-information representation.

Four modeling techniques, using topological descriptors to represent molecular structure, were employed to produce models of human serum protein binding (% bound) on a data set of 1008 experimental values, carefully screened from publicly available sources. To our knowledge, this data is the largest set on human serum protein binding reported for QSAR modeling. The data was partitioned into a training set of 808 compounds and an external validation test set of 200 compounds.

QSAR modeling of carcinogenic risk using discriminant analysis and topological molecular descriptors.

A discriminant analysis model is presented for carcinogenic risk. The data set is obtained from the two-year rodent study FDA/CDER database and was divided into a training set of 1022 organic compounds and an external validation test set of 50 compounds. The model is designed to use as a decision support tool for a defined decision threshold, and is thus a binary discrimination into "high risk" and "low risk" categories.

New predictors for several ADME/Tox properties: aqueous solubility, human oral absorption, and Ames genotoxicity using topological descriptors.

In silico predictive models for aqueous solubility, human intestinal absorption (HIA), and Ames genotoxicity were developed principally using artificial neural net (ANN) analysis and topological descriptors. Approximately 10,000 compounds spread across three data sets were used in the construction of these quantitative-structure-activity/property-relationship (QSAR/QSPR) models. For aqueous solubility, 5,037 chemically diverse compounds were used to construct ANN-QSPRs for intrinsic aqueous solubility.

Three new consensus QSAR models for the prediction of Ames genotoxicity.

Three QSAR methods, artificial neural net (ANN), k-nearest neighbors (kNN), and Decision Forest (DF), were applied to 3363 diverse compounds tested for their Ames genotoxicity. The ratio of mutagens to non-mutagens was 60/40 for this dataset. This group of compounds includes >300 therapeutic drugs.

Modeling drug albumin binding affinity with e-state topological structure representation.

The binding affinity to human serum albumin for 94 drugs was modeled with topological descriptors of molecular structure, using as experimental data the HPLC chromatographic retention index [logk(HSA)] on immobilized albumin. The electrotopological state (E-State) along with the molecular connectivity chi indices provided the basis for a satisfactory model: r(2) = 0.77, s = 0.

QSAR modeling of beta-lactam binding to human serum proteins.

The binding of beta-lactams to human serum proteins was modeled with topological descriptors of molecular structure. Experimental data was the concentration of protein-bound drug expressed as a percent of the total plasma concentration (percent fraction bound, PFB) for 87 penicillins and for 115 beta-lactams. The electrotopological state indices (E-State) and the molecular connectivity chi indices were found to be the basis of two satisfactory models.

Modeling blood-brain barrier partitioning using the electrotopological state.

The challenging problem of modeling blood-brain barrier partitioning is approached through topological representation of molecular structure. A QSAR model is developed for in vivo blood-brain partitioning data treated as the logarithm of the blood-brain concentration ratio. The model consists of three structure descriptors: the hydrogen E-State index for hydrogen bond donors, HS(T)(HBd); the hydrogen E-State index for aromatic CHs, HS(T)(arom); and the second order difference valence molecular connectivity index, d(2)chi(v) (q(2) = 0.

E-state modeling of HIV-1 protease inhibitor binding independent of 3D information.

Data for HIV-1 protease inhibitors (in vitro enzyme binding) were used as a training set to develop a QSAR model based on topological descriptors, including two hydrogen E-state indices, along with a molecular connectivity chi and a kappa shape index. A statistically satisfactory four-variable model was obtained for the 32 compounds in the training set, r2 = 0.86, s = 0.