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Predicting protein druggability is a key interest in the target identification phase of drug discovery. Here, we assess the pocket estimation methods' influence on druggability predictions by comparing statistical models constructed from pockets estimated using different pocket estimation methods: a proximity of either 4 or 5.5 Å to a cocrystallized ligand or DoGSite and fpocket estimation methods. We developed PockDrug, a robust pocket druggability model that copes with uncertainties in pocket boundaries. It is based on a linear discriminant analysis from a pool of 52 descriptors combined with a selection of the most stable and efficient models using different pocket estimation methods. PockDrug retains the best combinations of three pocket properties which impact druggability: geometry, hydrophobicity, and aromaticity. It results in an average accuracy of 87.9% ± 4.7% using a test set and exhibits higher accuracy (∼5-10%) than previous studies that used an identical apo set. In conclusion, this study confirms the influence of pocket estimation on pocket druggability prediction and proposes PockDrug as a new model that overcomes pocket estimation variability.

An algorithm is presented, which exhibits a computed number of rigid conformers of an input small molecule, covering the geometric diversity in the conformational space, with minimal structural redundancy. The algorithm calls a conformer generator, then performs an agglomerative hierarchical clustering with the modified clustering gain as the stop criterion. The number of classes is computed without an arbitrary parameter. A representative conformer is selected in each class, and nonrepresentative conformers are discarded. For illustration, the algorithm has been applied on a database containing 70 ligands of the cytochrome CYP 3A4, showing that the structural flexibility of each ligand is indeed handled via a small number of its representative conformers. The method is valid for all small molecules.

We introduce the principles and the architecture of a user-friendly software named MOLDIA (Molecular Diversity Analysis) which aims to the comparison of diverse molecular data sets through an XML structured database of predefined fragments. The MOLDIA descriptors are composed of complex fingerprint-like structures, which enclose not only structural information but also physicochemical property data. The system architecture includes the use of customizable weights on molecular descriptors and different choices of similarity/diversity measures to analyze the given data sets. Intermolecular comparisons using Ullmann's algorithm were optimized by the use of fuzzy logic, generic atoms, and a whole system of chemical graph analysis. We have found that customizing the similarity/diversity computation using structural and/or properties weights and choosing the level of fuzziness of the molecular comparison allow the user to adapt the tool to particular needs and increases the possibilities of MolDiA applications. The implementation of XML Web technologies has proven to improve and ease the extraction, processing, and analysis of chemical information.