• QSPR models were developed based on in vitro corneal permeability measurements of 189 compounds. Partial least squares regression was applied for the model building. Classical molecular descriptors and ECFP fingerprints used. Corneal membrane retention values predicted by models. The capability to predict physicochemical parameters has always been a desirable objective ophthalmic drug develop...

We present a novel probabilistic mean quantitative structure–property relationship (M-QSPR) method for the prediction of jet fuel properties considering two-dimensional gas chromatography measurements. Fuels are represented as one pseudo-structure that is inferred by weighted average over structures 1866 molecules could be in individual fuel. The allows training models on both data pure compone...

A greater number of toxicity data are becoming publicly available allowing for in silico modeling. However, questions often arise as to how to incorporate data quality and how to deal with contradicting data if more than a single datum point is available for the same compound. In this study, two well-known and studied QSAR/QSPR models for skin permeability and aquatic toxicology have been inves...

The search for multivariate linear regression (MLR) in quantitative structure-property relationships (QSPR) is a hard problem, due to the dimension of the entire search space. A genetic algorithm (GA) was developed and assessed, to select proper descriptors for predicting the octan-1-ol/H2O partition coefficient of polychlorinated biphenyls. The GA was implemented as a Windows based FreePascal ...

The problem of predicting the rate of percutaneous absorption of a drug is an important issue, particular with the increasing use of the skin as a means of moderating and controlling drug delivery. One key feature of this problem domain is that human skin permeability to penetrants (often characterised by Kp, the permeability coefficient) has been shown to be inherently non-linear when mathemat...

The support vector machine (SVM), as a novel type of a learning machine, for the first time, was used to develop a QSPR model that relates the structures of 35 amino acids to their isoelectric point. Molecular descriptors calculated from the structure alone were used to represent molecular structures. The seven descriptors selected using GA-PLS, which is a sophisticated hybrid approach that com...

In this study, the relationships between the brain-blood concentration ratio of 96 structurally diverse compounds with a large number of structurally derived descriptors were investigated. The linear models were based on molecular descriptors that can be calculated for any compound simply from a knowledge of its molecular structure. The linear correlation coefficients of the models were optimiz...

A quantitative structure–property relationship (QSPR) was developed, aiming to estimate the gas-phase enthalpies of formation (DfH ) of a set of 132 organometallic compounds of general formula MRnXn m, where M is a metal or a semimetal from groups 12 to 16, R is an alkyl, aryl, alkenyl, or alkynyl group, and X is Cl, Br, I, or H. The proposed model, derived from multilinear regression, contains...