Two personal perspectives on a key issue in contemporary 3D QSAR

Chemists working with small molecules are under enormous pressure to be able to reliably predict how biological systems in particular and the environment in general will respond to the deployment of the corresponding compounds as medicines, cosmetics, or in other manufactured goods. To be specific and robust, any such prediction must be based on an implicit or explicit mathematical model of how chemical structure relates to biological activity—i.e., on some postulated quantitative structure–activity relationship (QSAR). Such models are necessarily limited in how broadly they can be applied. Their applicability domain depends on the structural diversity of the data set used, but also on the descriptors used to characterize how that structural variation relates to the activity in question. In principle, descriptors based on the molecular interaction fields produced by atoms distributed in three‐dimensional (3D) space should be the most general of all, but finding suitable conformations and alignment is a challenge. One way to obtain these is by taking the structure of the macromolecular target into account as well, as is done in scoring ligand/receptor complexes for virtual screening. Unfortunately, the available docking tools are generally not up to the task. Here, we share some personal observations and opinions on two possible ways to address this shortcoming: implicitly, by iterative rescoring of docked poses obtained using derived 3D QSARs; and explicitly, by evaluating ligand interaction fields with respect to target atoms rather than against generalized probe atoms. © 2011 John Wiley & Sons, Ltd. This article is categorized under: Computer and Information Science > Chemoinformatics