Issues in Computational Modeling of Molecular Structure-Property Relationships in Real-World Settings

The problem of modeling structure-property relationships is a fundamental one in contemporary biology and drug discovery. An accurate model can not only be used to predict the behavior of a molecule and understand how structural variations may influence molecular property, but also to identify regions of molecular space that hold promise in context of a specific investigation. However, a variety of factors contribute to the difficulty of constructing robust structure activity models in real-world problems. These include conceptual issues related to how well the true biological problem is accounted for in the computational solution, algorithmic issues associated with determining the proper molecular descriptors, access to small quantities of data (possibly on tens of molecules only) due to the high cost and complexity of the experimental process, and the complex nature of bio-chemical phenomena underlying the data. This paper attempts to address this problem from the rudiments. We first identify and discuss the salient computational issues that span (and complicate) structure-property modeling formulations. We then consider a specific problem: that of modeling intestinal drug absorption, where many of the aforementioned factors play a role. In addressing them, our solution uses a novel characterization of molecular space based on the notion of surface-based molecular similarity. This is followed by identifying a statistically relevant set of molecular descriptors, which along with an appropriate machine learning technique is used to build the structure-property model. To ensure robustness, we propose simultaneous use of both ratio and ordinal error-measures for model construction and validation. The applicability of the approach is demonstrated through the predictive capability of the model in real world situations.