Multiobjective optimization of combinatorial libraries

Combinatorial chemistry and high-throughput screening have caused a fundame ntal shift in the way chemists contemplate experiments. Designing a combina torial library is a controversial art that involves a heterogeneous mix of chemistry, mathematics, economics, experience, and intuition. Although ther e seems to be little agreement as to what constitutes an ideal library, one thing is certain: Only one property or measure seldom defines the quality of the design. In most real-world applications, a good experiment requires the simultaneous optimization of several, often conflicting, design objecti ves, some of which may be vague and uncertain. In this paper, we discuss a class of algorithms for subset selection rooted in the principles of multio bjective optimization. Our approach is to employ an objective function that encodes all of the desired selection criteria, and then use a simulated an nealing or evolutionary approach to identify the optimal (or a nearly optim al) subset from among the vast number of possibilities. Many design criteri a can be accommodated, including diversity, similarity to known actives, pr edicted activity and/or selectivity determined by quantitative structure-ac tivity relationship (QSAR) models or receptor binding models, enforcement o f certain property distributions, reagent and availability, and many others . The method is robust, convergent, and extensible, offers the user full co ntrol over the relative significance of the various objectives in the final design, and permits the simultaneous selection of compounds from multiple libraries in full- or sparse-array format.