Bayesian Classification of Cytochrome P450 3A4 Substrates/Non-substrates and Color Mapping for Chemical Interpretation

Multivariate Analysis of Side Effects of Drug Molecules Based on Knowledge of Protein Bindings and ProteinProtein Interactions

Here, we examined the relationships between 969 side effects associated with 658 drugs and their 1368 human protein targets using our hybrid approaches. Firstly, L-shaped PLS (LPLS) was used to construct a multivariate model of side effects and protein bindings of drug molecules. LPLS is an extension of standard PLS regression, where, in addition to the response matrix Y and the regressor matrix X, an extra data matrix Z is constructed that summarizes the background information of X. X and Y are matrices comprising drugs-target proteins, and drugs-side effects, respectively. The Z matrix is the proteinprotein interaction data. From the loading plot of Y, we could identify two remarkable side effects (urinary incontinence and increased salivation) From the corresponding loading plot of X, the responsible protein targets causing each side effect could be estimated (sodium channels and gamma-aminobutyric acid (GABA) receptors). The loading plot of the Z matrix indicated that the GABA receptors interact with each other and they heavily influence the side effect of increased salivation. Secondly, Bayesian classifier methods were separately applied to the cases of the two side effects. That is, the Bayesian classifier method was used to classify drug molecules as binding or not binding to the responsible protein targets associated with each side effect. Using atom-coloring techniques, it was possible to estimate which fragments on the drug molecule might cause the specific side effects. This information is valuable for drug design to avoid specific side effects.

Data Mining of Chemogenomics Data Using Bi-Modal PLS Methods and Chemical Interpretation for Molecular Design

Chemogenomics is a new strategy in drug discovery for interrogating all molecules capable of interacting with all biological targets. Because of the almost infinite number of drug-like organic molecules, bench-based experimental chemogenomics methods are not generally feasible. Several in silico chemogenomics models have therefore been developed for high-throughput screening of large numbers of drug candidate compounds and target proteins. In previous studies, we described two novel bi-modal PLS approaches. These methods provide a significant advantage in that they enable direct connections to be made between biological activities and ligand and protein descriptors. In this special issue, we review these two PLS-based approaches using two different chemogenomics datasets for illustration. We then compare the predictive and interpretive performance of the two methods using the same congeneric data set.

Importance of physicochemical properties for the design of new pesticides

The physicochemical properties of candidate compounds play important roles in the design of new pesticides. Pesticides must be absorbed by pests, be transported to the target site, and then interact with proteins. Hydrophobicity is very important for these processes. Log P, where P is the partition coefficient in the 1-octanol/water system, is commonly used as a hydrophobic descriptor and correlates with membrane permeation and transport. It was recently reported that permeability by the parallel artificial membrane permeation assay (PAMPA) could be used to predict human oral absorption of passively transported compounds. PAMPA, which is a rapid high-throughput screening system, may be useful to predict pesticide absorption because PAMPA permeability can be calculated using log P and other parameters. Electronic and structural properties as well as hydrophobicity are important factors for protein-ligand interaction. To show the importance of physicochemical properties, the classic QSAR and CoMFA of neonicotinoids and prediction of bioavailability of pesticides in terms of membrane permeability in comparison with drugs are described.