Summary: Journal of Computer-Aided Molecular Design, 16: 335356, 2002.
© See footnote. Printed in the Netherlands.
Multiobjective optimization of combinatorial libraries
3-Dimensional Pharmaceuticals, Inc., 665 Stockton Drive, Suite 104, Exton, Pennsylvania 19341 USA
Combinatorial chemistry and high-throughput screening have caused a fundamental shift in the way chemists
contemplate experiments. Designing a combinatorial library is a controversial art that involves a heterogeneous
mix of chemistry, mathematics, economics, experience, and intuition. Although there 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 objectives, 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 multiobjective optimization. Our approach is to
employ an objective function that encodes all of the desired selection criteria, and then use a simulated annealing
or evolutionary approach to identify the optimal (or a nearly optimal) subset from among the vast number of
possibilities. Many design criteria can be accommodated, including diversity, similarity to known actives, predicted
activity and/or selectivity determined by quantitative structure-activity relationship (QSAR) models or receptor
binding models, enforcement of certain property distributions, reagent cost and availability, and many others. The