Comparative molecular field analysis of chromatographic hydrophobicity indices for some coumarin analogs

The chromatographic hydrophobicity index (CHI) is an HPLC‐based parameter that provides reliable guidance in optimization of pharmacological efficiency and adsorption, distribution, metabolism and exertion (ADME) profile of drug candidates. In the present work, classical and three‐dimensional quantitative structure–property relationship (QSPR) models were developed for prediction of CHI values of some 4‐hydroxycoumarin analogs on immobilized artificial membrane column. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) as 3D–QSPR methods were performed to gain insight into the key structural factors affecting on the chromatographic hydrophobicity of interested chemicals. The calculated parameters of Q ², R ² and standard error were 0.545, 0.996 and 0.773 for CoMFA model and 0.815, 0.986 and 1.44 for CoMSIA model, respectively. The contour maps for steric fields of the CoMFA model illustrate that the hydrophobicity of chemicals will be higher when the positions of R6, R7 and R8 in the 4‐hydroxycuomarin ring are substituted by alkyl groups. Moreover, by the analysis of the plots of electrostatic fields, it was concluded that the CHI value greatly increases if one hydrogen on coumarin ring is substituted by the F, Cl, Br, OH or OCH₃ group.     

Reduced variable molecular dynamics

This article describes an extension to previously developed constraint techniques. These enhanced constraint methods will enable the study of large computational chemistry problems that cannot be easily handled with current constrained molecular dynamics (MD) methods. These methods are based on an O(N) solution to the constrained equations of motion. The benefits of this approach are that (1) the system constraints are solved exactly at each time step, (2) the solution algorithm is noniterative, (3) the algorithm is recursive and scales as O(N), (4) the algorithm is numerically stable, (5) the algorithm is highly amenable to parallel processing, and (6) potentially greater integration step sizes are possible. It is anticipated that application of this methodology will provide a 10- to 100-improvement in the speed of a large molecular trajectory as compared with the time required to run a conventional atomistic unconstrained simulation. It is, therefore, anticipated that this methodology will provide an enabling capacity for pursuing the drug discovery process for large molecular systems. © 1995 John Wiley & Sons, Inc.     

Prediction of λmax of 1,4-naphthoquinone derivatives using ant colony optimization

Ant colony optimization (ACO) is a meta-heuristic algorithm, which is derived from the observation of real ants. In this paper, ACO algorithm is proposed to feature selection in quantitative structure property relationship (QSPR) modeling and to predict λ_max of 1,4-naphthoquinone derivatives. Feature selection is the most important step in classification and regression systems. The performance of the proposed algorithm (ACO) is compared with that of a stepwise regression, genetic algorithm and simulated annealing methods. The average absolute relative deviation in this QSPR study using ACO, stepwise regression, genetic algorithm and simulated annealing using multiple linear regression method for calibration and prediction sets were 5.0%, 3.4% and 6.8%, 6.1% and 5.1%, 8.6% and 6.0%, 5.7%, respectively. It has been demonstrated that the ACO is a useful tool for feature selection with nice performance.