Home My account Select a country Journals Home ... Contact Us Related link: IBM Life Sciences Volume 45, Numbers 3/4, 2001
Deep computing for the life sciences HTML PDF ASCII This article: HTML PDF ASCII Multiobjective optimization of combinatorial libraries by D. K. Agrafiotis 1. Introduction Prior to the advent of combinatorial chemistry, this process involved a simple prioritization of synthetic targets based on pre-existing structure-activity data, synthetic feasibility, experience, and intuition. This situation began to change as advances in parallel synthesis and high-throughput screening have enabled the simultaneous synthesis and biological evaluation of large chemical libraries containing hundreds to tens of thousands of compounds [ ]. Although throughput has increased dramatically, the number of compounds that can be created and tested in a reliable manner represents a tiny fraction of all the molecules of potential pharmaceutical interest, and the process is still fundamentally based on trial and error. It is becoming increasingly apparent that in order to maximize the probability of identifying sustainable drug candidates, combinatorial experiments must be carefully planned and take full advantage of whatever information is available about the biological target of interest. Whether it is used for lead discovery or optimization, the design of a good combinatorial library is a complex task that requires the simultaneous optimization of several, often conflicting, design objectives. In this paper, we present an overview of a general methodology for designing combinatorial and high-throughput screening experiments rooted in the principles of multiobjective optimization. | |
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