ACS Fall

Lead optimization efforts are guided by a combination of factors, among which, the lead’s potency, and its ADME/Tox properties play the major roles. Each drug discovery project aims at optimizing activity against specific target, however, computational models for the multitude of target affinity endpoints are not readily available. Consequently, conventional in silico lead optimization techniques can only be used for ADME/Tox profiling, while potency is neglected. In this work we present an Auto-SAR approach to overcome this issue by incorporating user-defined potency data in analog profiling. This approach is based on automatic Free-Wilson type SAR analysis on a series of known compounds with a common scaffold and varying substituents, to evaluate the influence of substituents in different positions on the considered property. The substituents are represented by their contributions to major physicochemical properties, such as size, lipophilicity, ionization, and hydrogen bonding. Exploring physicochemical dependences allows obtaining feasible, mechanistically interpretable class-specific SAR models from small data sets (several tens of compounds with measured potency data). Modeling involves special statistical methods to capture the nonlinearities in the relationship between the dependent property and used descriptors. The obtained class-specific models can be utilized to gain better understanding of substituent effects, evaluate target activities of new compounds of the same class, and guide lead optimization efforts to the most promising candidates. Finally, we present several case studies based on published lead optimization articles, where the structural analogs suggested by the software are compared to those proposed by the authors of the original studies.

In this study, we present an extension of recently published GALAS modeling methodology to develop QSAR models of aquatic toxicity accounting for mechanistic knowledge in terms of mode of toxic action (MOA) data. The endpoints considered for analysis were median lethal concentration of test compound in water (LC50) to fathead minnows (Pimephales promelas) and 50% inhibitory growth concentration (IGC50) to protozoan Tetrahymena pyriformis. The experimental data used for building the models involved 579 LC50 values for fishes obtained from PubChem database, as well as protozoan IGC50 data for 1093 compounds received from CADASTER project. Modeling was performed in a two-step approach consistent with baseline and excess toxicity concepts. In the first step, a global linear regression model was derived, describing baseline toxicity levels as a function of the compound’s lipophilicity, expressed by octanol/water logP. The second step involved analysis of systematic deviations introduced by the baseline model in the local chemical neighborhood of the compound, in order to account for excess toxicity of reactive chemicals. External validation of the obtained models demonstrated good predictive power with RMSE under 0.7 log units for estimating toxicity in both species. A further boost in model performance was observed, when an substructure-based expert system was used to classify chemicals by their MOA, so that only the compounds with the same MOA were considered in local analysis. Furthermore, the described models comply with OECD validation principles and are fully compatible with automatic generation QPRF reports.

Sharing is a fundamental of part of our early development in part because it helps maintain civility, but primarily because it broadens our experiences. In the scientific world sharing information is one of the key factors in the advancement of science. Fortunately for IR spectroscopists and to a slightly lesser extent Raman Spectroscopists, there exists a number of excellent text books where the authors have shared with us their compiled information about spectrum structure correlations. These correlations help us understand and interpret the spectra we work with every day.

In a corporate environment it is common for individuals to have detailed knowledge about the correlation between some spectral features and the materials they work with every day. This knowledge can often exceed the more general information available in the text books. However that knowledge generally resides in the mind of the individuals or sometimes is hand written on a chart and filed away. There has been no other method available to capture that knowledge. This paper will look at a method available which allows a spectroscopist to capture their knowledge in a manner allowing sharing of the information across the corporate environment and with future spectroscopists.