C. John Blankley

Comparison of chemical clustering methods using graph- and fingerprint-based similarity measures

This paper compares several published methods for clustering chemical structures, using both graph- and fingerprint-based similarity measures. The clusterings from each method were compared to determine the degree of cluster overlap. Each method was also evaluated on how well it grouped structures into clusters possessing a non-trivial substructural commonality. The methods which employ adjustable parameters were tested to determine the stability of each parameter for datasets of varying size and composition. Our experiments suggest that both graph- and fingerprint-based similarity measures can be used effectively for generating chemical clusterings; it is also suggested that the CAST and Yin-Chen methods, suggested recently for the clustering of gene expression patterns, may also prove effective for the clustering of 2D chemical structures.

Prediction of blood-brain partitioning using Monte Carlo simulations of molecules in water

The brain-blood partition coefficient (logBB) is a determining factor for the efficacy of central nervous system acting drugs. Since large-scale experimental determination of logBB is unfeasible, alternative evaluation methods based on theoretical models are desirable. Toward this direction, we propose a model that correlates logBB with physically significant descriptors for 76 structurally diverse molecules. We employ Monte Carlo simulations of the compounds in water to calculate such properties as the solvent-accessible surface area (SASA), the number of hydrogen bond donors and acceptors, the solute dipole, and the hydrophilic, hydrophobic and amphiphilic components of SASA. The physically significant descriptors are identified and a quantitative structure-prediction relationship is constructed that predicts logBB. This work demonstrates that computer simulations can be employed in a semi-empirical framework to build predictive QSPRs that shed light on the physical mechanism of biomolecular phenomena.