The effect of the diversity of molecules in sets and similarity of sets on the quality of prediction in QSAR studies

We report here: (a) formulas/procedures for calculating the similarity of molecules, considering their chemical structure, size, shape and hydrophilicity (b) a procedure for clusterization of the sets of molecules, according to similarity (c) formulas/procedures for calculating the diversity of molecules in clusterized sets as well as similarity of clusterized sets, based on Shannon Entropy formalism The paper analyses the influence of the diversity of molecules and similarity of calibration/prediction sets on the quality of prediction for prediction set molecules. The calculated influence of certain molecular feature (chemical structure, size, shape and hydrophilicity) on toxicity depends on the structure of the database, specifically the number of molecules and diversity of molecules having analyzed molecular feature. A QSAR analysis of 49 phenol derivatives revealed the effect of the diversity of molecules in sets and of the similarity of sets on the quality of prediction for prediction set molecules: (a) a direct correlation with the similarity of sets, regardless of analyzed molecular feature (b) an inverse correlation with the diversity of molecules in the calibration set, from the point of view of chemical structure, size and shape (c) a direct correlation with the diversity of molecules in calibration set, from the point of view of hydrophilicity (d) a direct correlation with the diversity of molecules in prediction set, regardless of analyzed feature.     

External validation and prediction employing the predictive squared correlation coefficient test set activity mean vs training set activity mean

The external prediction capability of quantitative structure-activity relationship (QSAR) models is often quantified using the predictive squared correlation coefficient, q (2). This index relates the predictive residual sum of squares, PRESS, to the activity sum of squares, SS, without postprocessing of the model output, the latter of which is automatically done when calculating the conventional squared correlation coefficient, r (2). According to the current OECD guidelines, q (2) for external validation should be calculated with SS referring to the training set activity mean. Our present findings including a mathematical proof demonstrate that this approach yields a systematic overestimation of the prediction capability that is triggered by the difference between the training and test set activity means. Example calculations with three regression models and data sets taken from literature show further that for external test sets, q (2) based on the training set activity mean may become even larger than r (2). As a consequence, we suggest to always use the test set activity mean when quantifying the external prediction capability through q (2) and to revise the respective OECD guidance document accordingly. The discussion includes a comparison between r (2) and q (2) value ranges and the q (2) statistics for cross-validation.     

Effectiveness of a short training session for improving pipetting accuracy

The quality of diagnostic tests conducted by the Animal Quarantine Service (AQS) is critical to the safety of national livestock production. In procedures for tests such as polymerase chain reaction and enzyme-linked immunosorbent assay, measurement of tiny quantities of samples or reagents is quite common. Therefore, many efforts have been made to maintain the quality of micropipette operations, including routine calibration of equipment and training programs for operators. In this study, we developed a pilot training program to analyze the effects of training on pipetting accuracy, i.e., trueness and precision. The program involved a self-instruction A4-sized leaflet describing how to improve pipetting technique. Thirty-three staff members from seven AQS laboratories participated in the program. Each participant repeated sampling of 300 μL of distilled water 10 times before and after reading the leaflet, and the effects of training on the trueness and precision of the pipetting results were analyzed. To determine pipetting trueness, we examined the variation of the delivered volume from the target volume (300 μL) using the proportional odds mixture model. Our data demonstrated that participation in the training program and the level of experience with pipetting significantly improved trueness. Analysis of standard deviations of the ten samplings, as an indicator of precision, also showed that participation in the training program improves pipetting precision. Thus, our data supported the implementation of such simple, quick training sessions to improve pipetting trueness and precision in diagnostic laboratories.     

Basis set approach to solution of poisson equation for small molecules immersed in solvent

The problem of how to calculate the electrostatic interactions between molecules and a solvent is a very important one in theoretical chemistry and biophysics. One of the more commonly used methods has been to represent the solvent by a dielectric continuum and then to solve the Poisson (or the Poisson-Boltzmann) equation for the potential due to the charge distribution of the solute. The solution of the equation has, up to now, been largely carried out using finite-difference grid-based methods. In this article, we investigate the use of an alternative method, based on a basis set expansion of the potential. The choice of basis functions, the representation of the dielectric function and the accuracy that can be obtained are discussed and illustrated by example calculations on small molecules. © 1997 by John Wiley & Sons, Inc.     

Semiempirical prediction of the hyperpolarizabilities for cyanovinyl-substituted donor-acceptor molecules

We present here a systematically theoretical study on the nonlinearities and their structure-property relationship of cyanovinyl-substituted donor-acceptor molecules by virtue of semiem-pirical PM3/AM1-FF approach.Good consistency between measured and calculated hyperpolarizabil-ities is obtained.Results show that conformation has a significant effect on hyperpolarizabilities.The torsion angle change between two conjugated parts of the molecular systems can substantially alter the nonlinearities.The total amount of charge transfer difference from donor to acceptor has been introduced to understand the microscopic nature of the nonlinear optical properties for the title molecules.General guidelines may be sought out in the search of molecules with large values of β Some molecules with large molecular hyperpolarizabilities can be predicted by the optimization for the longer π-electron systems with both acceptor and donor groups.     

On the floating basis set model for studying the vibronic interaction in polyatomic molecules

The floating basis set model by Johnson and Weigang is reinvestigated using a general picture of the molecular hamiltonian. It is found that Johnson and Weigang, though considering explicitly that electrons move with the nuclei, ignored a contribution to the electronic wavefunction from the coupling of the vibrational and electronic motions of the electrons. The vibrational wavefunctions and energies of the molecule are also modified.     

Performance of (consensus) kNN QSAR for predicting estrogenic activity in a large diverse set of organic compounds

A novel method (in the context of quantitative structure-activity relationship (QSAR)) based on the k nearest neighbour (kNN) principle, has recently been introduced for the derivation of predictive structure-activity relationships. Its performance has been tested for estimating the estrogen binding affinity of a diverse set of 142 organic molecules. Highly predictive models have been obtained. Moreover, it has been demonstrated that consensus-type kNN QSAR models, derived from the arithmetic mean of individual QSAR models were statistically robust and provided more accurate predictions than the great majority of the individual QSAR models. Finally, the consensus QSAR method was tested with 3D QSAR and log P data from a widely used steroid benchmark data set.