Increasing Complexity: A Practical Synthetic Approach to Three-Dimensional, Cyclic Sulfoximines and First Insights into Their in Vitro Properties.

A short synthetic approach with broad scope to access five- to seven-membered cyclic sulfoximines in only two to three steps from readily available thiophenols is reported. Thus, simple building blocks were converted to complex molecular structures by a sequence of S-alkylation and one-pot sulfoximine formation, followed by intramolecular cyclization. Seventeen structurally diverse cyclic sulfoximines were prepared in high overall yields.

Exploration of Novel Chemical Space: Synthesis and in vitro Evaluation of N-Functionalized Tertiary Sulfonimidamides.

An unprecedented set of structurally diverse sulfonimidamides (47 compounds) has been prepared by various N-functionalization reactions of tertiary =NH sulfonimidamide 2 aa. These N-functionalization reactions of model compound 2 aa include arylation, alkylation, trifluoromethylation, cyanation, sulfonylation, alkoxycarbonylation (carbamate formation) and aminocarbonylation (urea formation). Small molecule X-ray analyses of selected N-functionalized products are reported.

Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules.

We investigate the use of different Machine Learning methods to construct models for aqueous solubility. Models are based on about 4000 compounds, including an in-house set of 632 drug discovery molecules of Bayer Schering Pharma. For each method, we also consider an appropriate method to obtain error bars, in order to estimate the domain of applicability (DOA) for each model.

Machine learning models for lipophilicity and their domain of applicability.

Unfavorable lipophilicity and water solubility cause many drug failures; therefore these properties have to be taken into account early on in lead discovery. Commercial tools for predicting lipophilicity usually have been trained on small and neutral molecules, and are thus often unable to accurately predict in-house data. Using a modern Bayesian machine learning algorithm--a Gaussian process model--this study constructs a log D7 model based on 14,556 drug discovery compounds of Bayer Schering Pharma.