Open3DQSAR: a new open-source software aimed at high-throughput chemometric analysis of molecular interaction fields.
ABSTRACT Open3DQSAR is a freely available open-source program aimed at chemometric analysis of molecular interaction fields. MIFs can be imported from different sources (GRID, CoMFA/CoMSIA, quantum-mechanical electrostatic potential or electron density grids) or generated by Open3DQSAR itself. Much focus has been put on automation through the implementation of a scriptable interface, as well as on high computational performance achieved by algorithm parallelization. Flexibility and interoperability with existing molecular modeling software make Open3DQSAR a powerful tool in pharmacophore assessment and ligand-based drug design.
Article: D-optimal designs[show abstract] [hide abstract]
ABSTRACT: Many classical symmetrical designs have desirable characteristics, one of which is called D-optimality. The D-optimality concept can also be applied to select a design when the classical symmetrical designs cannot be used, such as when the experimental region is not regular in shape, when the number of experiments chosen by a classical design is too large or when one wants to apply models that deviate from the usual first or second order ones. The D-optimality concept is developed and it is also explained that D-optimality is only one possible criterion to choose a particular design. A few other criteria are also given that complement the information obtained by the D-criterion.Chemometrics and Intelligent Laboratory Systems. 01/1995;
Article: LA-PACK Users''Guide
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ABSTRACT: A new method for the elimination of uninformative variables in multivariate data sets is proposed. To achieve this, artificial (noise) variables are added and a closed form of the PLS or PCR model is obtained for the data set containing the experimental and the artificial variables. The experimental variables that do not have more importance than the artificial variables, as judged from a criterion based on the b coefficients, are eliminated. The performance of the method is evaluated on simulated data. Practical aspects are discussed on experimentally obtained near-IR data sets. It is concluded that the elimination of uninformative variables can improve predictive ability.Analytical Chemistry 11/1996; 68(21):3851-8. · 5.70 Impact Factor