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Background: Local classification models were used to establish Quantitative Structure− Activity Relationships (QSARs) of bioactive di−, tri− and tetrapeptides, with their capacity to inhibit Angiotensin Converting Enzyme (ACE). These discrete models can thus predict this activity for other peptides obtained from functional foods. These types of peptides allow some foods to be considered nutraceuticals. Method: A database of 313 molecules of di−, tri− and tetrapeptides was investigated and antihypertensive activities of peptides, expressed as log (1/IC50), were separated into two qualitative classes: low activity (inactive) was associated with experimental values under the 66th percentile and active peptides with values above this threshold. Chemicals were divided into a training set, including 70% of the peptides, and a test set for external validation. Genetic algorithms-variable subset selection coupled with the kNN and N3 local classifiers were applied to select the best subset of molecular descriptors from a pool of 953 Dragon descriptors. Both models were validated on the test peptides. Results: The N3 model turned out to be superior to the kNN model when the classification focused on identifying the most active peptides.

Several classical and two recently proposed Applicability Domain (AD) approaches were implemented on a set of three classification models retrieved from a published study to assess the ready biodegradability of chemicals. Each model was associated with an optimal AD approach based on its ability to a) retain maximum test molecules within the model’s AD, b) be appropriate for the strategy used towards model development and c) show reasonably converging results with those derived with other AD approaches used. A decision criterion was also set to evaluate the AD of two consensus models that were developed in the original study. An overview of test molecules excluded from the AD of all the five biodegradability models was provided including an attempt to identify the major structural features and molecular descriptors possibly relevant in deciding upon their ready biodegradability. Apart from the test set, an overview of the results derived on the external validation set molecules was provided.

In this study, a QSAR model was developed from a data set consisting of 546 organic molecules, to predict acute aquatic toxicity toward Daphnia magna. A modified k-Nearest Neighbour (kNN) strategy was used as the regression method, which provided prediction only for those molecules with an average distance from the knearest neighbours lower than a selected threshold. The final model showed good performance (R2and Q2cvequal to 0.78, Q2extequal to 0.72). It comprised eight molecular descriptors that encoded information about lipophilicity, the formation of H-bonds, polar surface area, polaris-ability, nucleophilicity and electrophilicity.

In a previous paper the theory of the new molecular descriptors called GETAWAY(GEometry, Topology, and Atom-Weights AssemblY) was explained. These descriptors have been proposed with the aim of matching 3D-molecular geometry, atom relatedness, and chemical information. In this paper prediction ability in structure−property correlations of GETAWAY descriptors has been tested extensively by analyzing the regressions of these descriptors for selected properties of some reference compound classes. Moreover, the general performance of the new descriptors in QSAR/QSPR has been evaluated with respect to other well-known sets of molecular descriptors.

Novel molecular descriptors based on a leverage matrix similar to that defined in statistics and usually used for regression diagnostics are presented. This leverage matrix, called Molecular Influence Matrix(MIM), is here proposed as a new molecular representation easily calculated from the spatial coordinates of the molecule atoms in a chosen conformation. The proposed molecular descriptors are called GETAWAY(GEometry, Topology, and Atom-Weights AssemblY) as they try to match 3D-molecular geometry provided by the molecular influence matrix and atom relatedness by molecular topology, with chemical information by using different atomic weightings (atomic mass, polarizability, van der Waals volume, and electronegativity, together with unit weights). A first set of molecular descriptors, called H-GETAWAY, is derived by using only the information provided by the molecular influence matrix, while a second set, called R-GETAWAY, combines this information with geometric interatomic distances in the molecule. The prediction ability in structure−property correlations of the new descriptors was tested by analyzing regressions of these descriptors for selected properties of octanes.

The role of multivariate analysis methods in evaluating, rationalizing, and working out complex environmental problems is discussed. The discussion is organized in two sections; a literature analysis of the application of chemometric methods to PCDD/PCDF data interpretation and source correlation and a review of the role of chemometric methods in analysing the results obtained by the Authors studying PCDD/PCDF formation and destruction mechanisms in MSW combustion processes.

The recently proposed WHIM (Weighted Holistic Invariant Molecular) approach [Todeschini,R., Lasagni, M. and Marengo, E., J. Chemometrics, 8 (1994) 263] has been applied tomolecular surfaces to derive new 3D theoretical descriptors, called MS-WHIM. To test theirreliability, a 3D QSAR study has been performed on a series of steroids, comparing the MS-WHIM description to both the original WHIM indices and CoMFA fields. The analysis of thestatistical models obtained shows that MS-WHIM descriptors provide meaningful quantitativestructure–activity correlations. Thus, the results obtained agree well with thoseachieved using CoMFA fields. The concise number of indices, the ease of their calculationand their invariance to the coordinate system make MS-WHIM an attractive tool for 3DQSAR studies.

Platinum microquantities were determined in plasma of patients affected by lung carcinoma during treatment with radiotherapy (RT) and concurrent low-dose continuous infusion ofcis-dichlorodiammineplatinum(II) (CDDP). RT was given at 50 Gy in continuous course; CDDP was continuously infused at 4 mg/m2 daily for 100h/week for 5 weeks, and the infusions were separated by 68h of rest. The percentage of free drug versus total drug in plasma was about 3%. It did not vary with therapy duration and was not significantly different from that found in 5-day continuous infusions at much higher daily doses. Never-theless, maximal values of free Pt in plasma were very low and agreed with the low level of CDDP toxicity encountered on the present administration schedule.

A 3D QSAR methodology based on the combined use of conformational analysis and chemometrics was applied to perform a comparative analysis of the 3D conformational features of 13 nonpeptide angiotensin II receptor antagonists showing different levels of binding affinity. Conformational analysis by using a molecular mechanics MM2 method was carried out for each of these structures to obtain conformational minima. These minima were described by ten interatomic distances which define the relative spatial disposition of five significant atoms belonging to relevant functional groups present in all the 13 molecules. The structure-activity relationship between the interatomic distances and the biological activity was then assessed by using chemometric methods (cluster analysis, principal component analysis, classification methods). With our indirect approach based on the search for geometrical similarity it was possible, even though structural information on the receptor active site was lacking, to identify the 3D geometrical requirements for the binding affinity of nonpeptide angiotensin II receptor inhibitors.

Hydroxylamine-induced cleavage at the asparaginyl–glycine dipeptide site inserted between the two moieties of recombinant fusion proteins has been used at both the analytical and the preparative scale to obtain the mature protein. In this study a model protein containing a fusion precursor of insulin-like growth factor I was used to investigate the influence of the operating conditions on the cleavage reaction and the formation of undesired side products such as hydroxamate and deamidated analogs. Moreover, the stability of the cleavage site toward deamidation was examined and a chemometric study performed to define the effect of the reaction conditions on the cleavage yield and on the formation of side products.

The ICCVAM Acute Toxicity Workgroup (U.S. Department of Health and Human Services), in collaboration with the U.S. Environmental Protection Agency (U.S. EPA, National Center for Computational Toxicology), coordinated the “Predictive Models for Acute Oral Systemic Toxicity” collaborative project to develop in silico models to predict acute oral systemic toxicity for filling regulatory needs. In this framework, new Quantitative Structure‐Activity Relationship (QSAR) models for the prediction of very toxic (LD50lower than 50 mg/kg) and nontoxic (LD50greater than or equal to 2,000 mg/kg) endpoints were developed, as described in this study. Models were developed on a large set of chemicals (8992), provided by the project coordinators, considering the five OCED principles for QSAR applicability to regulatory endpoints. A Bayesian consensus approach integrating three different classification QSAR algorithms was applied as modelling method. For both the considered endpoints, the proposed approach demonstrated to be robust and predictive, as determined by a blind validation on a set of external molecules provided in a later stage by the coordinators of the collaborative project. Finally, the integration of predictions obtained for the very toxic and nontoxic endpoints allowed the identification of compounds associated to medium toxicity, as well as the analysis of consistency between the predictions obtained for the two endpoints on the same molecules. Predictions of the proposed consensus approach will be integrated with those originated from models proposed by the participants of the collaborative project to facilitate the regulatory acceptance of in‐silico predictions and thus reduce or replace experimental tests for acute toxicity.

Quantitative Structure – Activity Relationship (QSAR) models play a central role in medicinal chemistry, toxicology and computer‐assisted molecular design, as well as a support for regulatory decisions and animal testing reduction. Thus, assessing their predictive ability becomes an essential step for any prospective application. Many metrics have been proposed to estimate the model predictive ability of QSARs, which have created confusion on how models should be evaluated and properly compared. Recently, we showed that the metric QF32is particularly well‐suited for comparing the external predictivity of different models developed on the same training dataset. However, when comparing models developed on different training data, this function becomes inadequate and only dispersion measures like the root‐mean‐square error (RMSE) should be used. The intent of this work is to provide clarity on the correct and incorrect uses of QF32, discussing its behavior towards the training data distribution and illustrating some cases in which QF32estimates may be misleading. Hereby, we encourage the usage of measures of dispersions when models trained on different datasets have to be compared and evaluated.

Molecular descriptors capture diverse structural information of molecules and are a prerequisite for ligand‐based similarity searching. In this study, we introduce topological matrix‐based descriptors to virtual screening for hit discovery. We evaluated the usefulness of matrix‐based descriptors in a retrospective setting and compared them with topological pharmacophore descriptors. Special attention was given to the influence of data pre‐processing and the applied similarity metric on the virtual screening performance. Overall, the MB descriptors showed a competitive and complementary performance to other descriptors. A prospective screen of a commercial compound library led to the discovery of a novel natural‐product‐derived cyclooxygenase‐2 inhibitor predicted to interact differently with the target protein compared to the query compound ibuprofen. The results of our study motivate the use of matrix‐based descriptors for molecular similarity‐based virtual screening and scaffold hopping.