Molecular library design using multi-objective optimization methods.
Advancements in combinatorial chemistry and high-throughput screening technology have enabled the synthesis and screening of large molecular libraries for the purposes of drug discovery. Contrary to initial expectations, the increase in screening library size, typically combined with an emphasis on compound structural diversity, did not result in a comparable increase in the number of promising hits found. In an effort to improve the likelihood of discovering hits with greater optimization potential, more recent approaches attempt to incorporate additional knowledge to the library design process to effectively guide the search. Multi-objective optimization methods capable of taking into account several chemical and biological criteria have been used to design collections of compounds satisfying simultaneously multiple pharmaceutically relevant objectives. In this chapter, we present our efforts to implement a multi-objective optimization method, MEGALib, custom-designed to the library design problem. The method exploits existing knowledge, e.g. from previous biological screening experiments, to identify and profile molecular fragments used subsequently to design compounds compromising the various objectives.
Nicolaou
Christos A
CA
Noesis Chemoinformatics, Nicosia, Cyprus. cnicolaou@cs.ucy.ac.cy
Kannas
Christos C
CC
D000465:Algorithms; D020650:Combinatorial Chemistry Techniques / Q000379:methods*; D055808:Drug Discovery / Q000379:methods*; D004353:Drug Evaluation, Preclinical; D054852:Small Molecule Libraries / Q000138:chemical synthesis / Q000737:chemistry / Q000494:pharmacology; D012984:Software
D054852:Small Molecule Libraries