A new approach to predict the biological activity of molecules based on similarity of their interaction fields and the logP and logD values: application to auxins

The activity of a biological compound is dependent both on specific binding to a target receptor and its ADME (Absorption, Distribution, Metabolism, Excretion) properties. A challenge to predict biological activity is to consider both contributions simultaneously in deriving quantitative models. We present a novel approach to derive QSAR models combining similarity analysis of molecular interaction fields (MIFs) with prediction of logP and/or logD. This new classification method is applied to a set of about 100 compounds related to the auxin plant hormone. The classification based on similarity of their interaction fields is more successful for the indole than the phenoxy compounds. The classification of the phenoxy compounds is however improved by taking into account the influence of the logP and/or the logD values on biological activity. With the new combined method, the majority (8 out of 10) of the previously misclassified derivatives of phenoxy acetic acid are classified in accord with their bioassays. The recently determined crystal structure of the auxin-binding protein 1 (ABP1) enabled validation of our approach. The results of docking a few auxin related compounds with different biological activity to ABP1 correlate well with the classification based on similarity of MIFs only. Biological activity is, however, better predicted by a combined similarity of MIFs + logP/logD approach.     

Assay Related Target Similarity (ARTS) - chemogenomics approach for quantitative comparison of biological targets

Computer-based chemogenomics approaches compare macromolecular drug targets based on their amino acid sequences or derived properties, by similarity of their ligands, or according to ligand-target interaction models. Here we present ARTS (Assay Related Target Similarity) as a quantitative index that estimates target similarity directly from measured affinities of a set of probe compounds. This approach reduces the risk of deducing artificial target relationships from mutually inactive compounds. ARTS implements a scoring scheme that matches intertarget similarity based on dose-response measurements. While all experimentally derived target similarities have a tendency to be data set-dependent, we demonstrate that ARTS depends less on the used data set than the commonly used Pearson correlation or Tanimoto index.