 Poster presentationMulti-objective de novo drug design using evolutionary graphsChristos A Nicolaou and CS Pattichis Computer Science Department, University of Cyprus, 75 Kallipoleos Str., CY-1678 Nicosia, Cyprus  corresponding author email
from 3rd German Conference on Chemoinformatics Goslar, Germany. 11-13 November 2007 Chemistry Central Journal 2008,
2(Suppl 1):P7doi:10.1186/1752-153X-2-S1-P7 First paragraph (this article has no abstract)
Drug discovery and development is a complex, lengthy process and failure of a candidate molecule can occur as a result of a combination of reasons, such as poor pharmacokinetics, lack of efficacy or toxicity. Drugs compromise the numerous, sometimes competing objectives so that the benefits to patients outweigh potential drawbacks and risks [1]. De novo drug design, involves searching an immense space of feasible, drug-like molecules to select those with the highest chances of becoming drugs using computational technology [2]. Traditionally, de novo design has focused on designing molecules satisfying a single objective, such as a similarity value to a known ligand or a virtual screening score, and ignored the presence of the multiple objectives required for drug-like behavior. Recently, methods have appeared in the literature that attempt to design molecules satisfying multiple predefined objectives [3]. In this presentation we briefly review these methods and then describe a new multi-objective optimization de novo design algorithm that combines evolutionary techniques with graph-theory to directly manipulate molecular graphs and design structurally diverse molecules satisfying one or more objectives. | 
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