Simulated molecular evolution in a full combinatorial library.

BACKGROUND The Darwinian concept of 'survival of the fittest' has inspired the development of evolutionary optimization methods to find molecules with desired properties in iterative feedback cycles of synthesis and testing. These methods have recently been applied to the computer-guided heuristic selection of molecules that bind with high affinity to a given biological target. We describe the optimization behavior and performance of genetic algorithms (GAs) that select molecules from a combinatorial library of potential thrombin inhibitors in 'artificial molecular evolution' experiments, on the basis of biological screening results. RESULTS A full combinatorial library of 15,360 members structurally biased towards the serine protease thrombin was synthesized, and all were tested for their ability to inhibit the protease activity of thrombin. Using the resulting large structure-activity landscape, we simulated the evolutionary selection of potent thrombin inhibitors from this library using GAs. Optimal parameter sets were found (encoding strategy, population size, mutation and cross-over rate) for this artificial molecular evolution. CONCLUSIONS A GA-based evolutionary selection is a valuable combinatorial optimization strategy to discover compounds with desired properties without needing to synthesize and test all possible combinations (i.e. all molecules). GAs are especially powerful when dealing with very large combinatorial libraries for which synthesis and screening of all members is not possible and/or when only a small number of compounds compared with the library size can be synthesized or tested. The optimization gradient or 'learning' per individual increases when using smaller population sizes and decreases for higher mutation rates.

[1]  Isao Karube,et al.  Directed evolution of trypsin inhibiting peptides using a genetic algorithm , 1996 .

[2]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[3]  W. Vent,et al.  Rechenberg, Ingo, Evolutionsstrategie — Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. 170 S. mit 36 Abb. Frommann‐Holzboog‐Verlag. Stuttgart 1973. Broschiert , 1975 .

[4]  Melanie Mitchell,et al.  Relative Building-Block Fitness and the Building Block Hypothesis , 1992, FOGA.

[5]  Philip M. Dean,et al.  Molecular diversity in drug design , 2002 .

[6]  Ingo Rechenberg,et al.  Evolutionsstrategie : Optimierung technischer Systeme nach Prinzipien der biologischen Evolution , 1973 .

[7]  L. Weber,et al.  Applications of genetic algorithms in molecular diversity. , 1998, Current opinion in chemical biology.

[8]  Gobbi,et al.  Genetic optimization of combinatorial libraries , 1998, Biotechnology and bioengineering.

[9]  L. Booker.  Foundations of genetic algorithms. 2: L. Darrell Whitley (Ed.), Morgan Kaufmann, San Mateo, CA, 1993, ISBN 1-55860-263-1, 322 pp., US$45.95 , 1994 .

[10]  L. Weber,et al.  Synthesis of Imidazo[1,2‐a] Annulated Pyridines, Pyrazines, and Pyrimidines by a Novel Three‐Component Condensation. , 1998 .

[11]  L. Weber,et al.  Synthesis of Imidazo[1,2-a] annulated Pyridines, Pyrazines and Pyrimidines by a Novel Three-Component Condensation , 1998 .

[12]  Edward P. Jaeger,et al.  Application of Genetic Algorithms to Combinatorial Synthesis: A Computational Approach to Lead Identification and Lead Optimization†,∇ , 1996 .

[13]  I. Ugi,et al.  Isonitrile, II. Reaktion von Isonitrilen mit Carbonylverbindungen, Aminen und Stickstoffwasserstoffsäure , 1961 .

[14]  Klaus Gubernator,et al.  Optimization of the Biological Activity of Combinatorial Compound Libraries by a Genetic Algorithm , 1995 .