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Agrafiotis, D. K. 1 IBM J. Res. Develop., Special Issue on Deep Computing in the Life Sciences, in press

Summary: Agrafiotis, D. K. 1
IBM J. Res. Develop., Special Issue on Deep Computing in the Life Sciences, in press
Dimitris K. Agrafiotis*
3-Dimensional Pharmaceuticals, Inc., 665 Stockton Drive, Exton, PA 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 on the
principles of multiobjective optimization. Our approach is to employ an objective function that encodes all
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. Virtually any conceivable
design criterion can be accommodated, including diversity, similarity to known actives, predicted activity
and/or selectivity as determined by some QSAR or receptor binding model, enforcement of certain property
distributions, reagent cost and availability, and many others. The method is robust, convergent, and
extensible, offers the user full control 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


Source: Agrafiotis, Dimitris K. - Molecular Design and Informatics Group, Johnson & Johnson Pharmaceutical Research and Development


Collections: Chemistry; Computer Technologies and Information Sciences