Multi-objective optimization methods in de novo drug design.

De-novo drug design (DND) is a complex procedure, requiring the satisfaction of many pharmaceutically important objectives. Several computational methodologies employing various optimization approaches have been developed to search for satisfactory solutions to this multi-objective problem varying from composite methods, which transform the problem to a single objective one to Pareto methods searching for numerous solutions compromising the objectives. In this review we initially focus on the DND problem and the challenges it poses to computational methods, followed by an examination of the reported methodologies and specific applications. Emphasis is placed on the multiobjective nature of the problem, related considerations and the solutions proposed by the drug discovery community.

Analysis of large screening data sets via adaptively grown phylogenetic-like trees.

As the use of high-throughput screening systems becomes more routine in the drug discovery process, there is an increasing need for fast and reliable analysis of the massive amounts of the resulting data. At the forefront of the methods used is data reduction, often assisted by cluster analysis. Activity thresholds reduce the data set under investigation to manageable sizes while clustering enables the detection of natural groups in that reduced subset, thereby revealing families of compounds that exhibit increased activity toward a specific biological target. The above process, designed to handle primarily data sets of sizes much smaller than the ones currently produced by high-throughput screening systems, has become one of the main bottlenecks of the modern drug discovery process. In addition to being fragmented and heavily dependent on human experts, it also ignores all screening information related to compounds with activity less than the threshold chosen and thus, in the best case, can only hope to discover a subset of the knowledge available in the screening data sets. To address the deficiencies of the current screening data analysis process the authors have developed a new method that analyzes thoroughly large screening data sets. In this report we describe in detail this new approach and present its main differences with the methods currently in use. Further, we analyze a well-known, publicly available data set using the proposed method. Our experimental results show that the proposed method can improve significantly both the ease of extraction and amount of knowledge discovered from screening data sets.