L. Mark Hall

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, So. 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. These models were based on 3343 aromatic and 1674 non‐aromatic compounds for training sets; 938 compounds were used in external validation testing. The range in −log So is −1.6 to 10. Topological structure descriptors were used with all models. A genetic algorithm was used for descriptor selection for regression models. For the artificial neural network (ANN) model, descriptor selection was done with a backward elimination process. All models performed well with r2 values ranging 0.72 to 0.84 in external validation testing. The mean absolute errors in validation ranged from 0.44 to 0.80 for the classes of compounds for all the models. These statistical results indicate a sound ANN model. Furthermore, in a comparison with eight other available models, based on predictions using a validation test set (442 compounds), the artificial neural network model presented in this work (CSLogWS) was clearly superior based on both the mean absolute error and the percentage of residuals less than one log unit. In the ANN model both E‐State and hydrogen E‐State descriptors were found to be important.

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 = 0.29, q(2) = 0.70, s(press) = 0.33. The 10% leave-group-out (LGO) cross-validation method yielded q(2) (= r(2)(press)) = 0.69. Further, the model was tested on a 10 compound external validation set, yielding a mean absolute error, MAE = 0.31; q(2) (= r(2)(press)) = 0.74. MDL QSAR software was used for setting up the data set, creation of combination descriptors, modeling, and database management. All the statistical tests indicate that the topological model is useful for property estimation. Internal and external validation methods were used, and the results indicate that the model is useful for prediction. Randomizations of the activity values also indicate statistically sound models are very different from random statistics. The model indicates that positive factors for binding affinity include electron accessibility and the number of aromatic rings, aliphatic CH groups (-CH(3), -CH(2)-, >CH-), halogens (fluorine and chlorine), and -OH groups. Five-membered heteroatomic rings present a negative factor, whereas six-membered heteroatomic rings present a positive factor. The specific information described can be used as an aid to the drug design process.

QSAR modeling of β-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 β-lactams. The electrotopological state indices (E-State) and the molecular connectivity chi indices were found to be the basis of two satisfactory models. A data set of 74 penicillins from a drug design series was successfully modeled with statistics: r2=0.80, s = 12.1, q2=0.76, spress=13.4. This model was then used to predict protein binding (PFB) for 13 commercial penicillins, resulting in a very good mean absolute error, MAE = 12.7 and correlation coefficient, q2=0.84. A group of 28 cephalosporins were combined with the penicillin data to create a dataset of 115 beta-lactams that was successfully modeled: r2=0.82, s = 12.7, q2=0.78, spress=13.7. A ten-fold 10% leave-group-out (LGO) cross-validation procedure was implemented, leading to very good statistics: MAE = 10.9, spress=14.0, q2 (or r2press)=0.78. The models indicate a combination of general and specific structure features that are important for estimating protein binding in this class of antibiotics. For the β-lactams, significant factors that increase binding are presence and electron accessibility of aromatic rings, halogens, methylene groups, and =N– atoms. Significant negative influence on binding comes from amine groups and carbonyl oxygen atoms.

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. The same input descriptors were used to develop models by both learning algorithms. The MLR model yielded marginally acceptable statistics with training correlation r(2) = 0.65, mean absolute error (MAE) = 83 RI units. External validation of 104 compounds not used for model development yielded validation v(2) = 0.49 and MAE = 73 RI units. The distribution of residuals for the fit and validate data sets suggest a nonlinear relationship between retention index and molecular structure as described by the SIR indices. Not surprisingly, the ANN model was significantly more accurate for both training and validation with training set r(2) = 0.93, MAE = 30 RI units and validation v(2) = 0.84, MAE = 41 RI units. For the ANN model, a total of 91% of validation predictions were within 100 RI units of the experimental value.

Methods for Predicting the Affinity of Drugs and Drug-Like Compounds for Human Plasma Proteins: A Review

Significant research has been conducted in the area of developing in silico methods for predicting the affinity of drugs and drug-like compounds for human plasma proteins. The free fraction of a compound associated with a given level of binding affinity has a significant impact on the pharmacokinetic profile of a drug and its metabolites. The development of quality plasma protein binding models has become an important goal in assisting the drug optimization process. The structure, binding sites and binding interaction modes of common plasma proteins are discussed along with the protein composition of human plasma and pharmacokinetic consequences of protein binding profiles. A short section outlines current methods for measuring binding affinity for plasma proteins. A total of eighteen published studies were reviewed for this article and the statistical results from 42 models are tabulated and compared. Models are compared on the basis of the endpoint modeled, method of structure description, learning algorithm, validation criteria, and statistical results. The role of logP as an input descriptor and the possible utility of reported models in categorizing virtual compounds are also discussed.

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 total of 1955 compounds were obtained and measured for the model. A separate set of 202 human metabolites was used for independent validation. The new ANN model showed improved accuracy over previous models. The model, based on relatively simple compounds, was able to make quality predictions for complex compounds not similar to training data. Independent validation metabolites with feature combinations found in three or more training compounds were predicted with 97% sensitivity while metabolites with feature combinations found in less than three training compounds were predicted with >90% sensitivity. The study describes the method used to select synthetic compounds and new descriptors developed to encode the relationship between lipophilic molecular subgraphs and HPLC retention. Finally, we introduce the QRI (qualitative range of interest) modification of neural network backpropagation learning to generate models simultaneously based on quantitative and qualitative data.

Evaluation of an Artificial Neural Network Retention Index Model for Chemical Structure Identification in Nontargeted Metabolomics

Liquid chromatography coupled with electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS) is a major analytical technique used for nontargeted identification of metabolites in biological fluids. Typically, in LC-ESI-MS/MS based database assisted structure elucidation pipelines, the exact mass of an unknown compound is used to mine a chemical structure database to acquire an initial set of possible candidates. Subsequent matching of the collision induced dissociation (CID) spectrum of the unknown to the CID spectra of candidate structures facilitates identification. However, this approach often fails because of the large numbers of potential candidates (i.e., false positives) for which CID spectra are not available. To overcome this problem, CID fragmentation predication programs have been developed, but these also have limited success if large numbers of isomers with similar CID spectra are present in the candidate set. In this study, we investigated the use of a retention index (RI) predictive model as an orthogonal method to help improve identification rates. The model was used to eliminate candidate structures whose predicted RI values differed significantly from the experimentally determined RI value of the unknown compound. We tested this approach using a set of ninety-one endogenous metabolites and four in silico CID fragmentation algorithms: CFM-ID, CSI:FingerID, Mass Frontier, and MetFrag. Candidate sets obtained from PubChem and the Human Metabolite Database (HMDB) were ranked with and without RI filtering followed by in silico spectral matching. Upon RI filtering, 12 of the ninety-one metabolites were eliminated from their respective candidate sets, i.e., were scored incorrectly as negatives. For the remaining seventy-nine compounds, we show that RI filtering eliminated an average of 58% from PubChem candidate sets. This resulted in an approximately 2-fold improvement in average rankings when using CFM-ID, Mass Frontier, and MetFrag. In addition, RI filtering slightly increased the occurrence of number one rankings for all 4 fragmentation algorithms. However, RI filtering did not significantly improve average rankings when HMDB was used as the candidate database, nor did it significantly improve average rankings when using CSI:FingerID. Overall, we show that the current RI model incorrectly eliminated more true positives (12) than were expected (4-5) on the basis of the filtering method. However, it slightly improved the number of correct first place rankings and improved overall average rankings when using CFM-ID, Mass Frontier, and MetFrag.