De novo drug design using multiobjective evolutionary graphs
Date
2009ISSN
1549-9596Source
Journal of Chemical Information and ModelingVolume
49Issue
2Pages
295-307Google Scholar check
Keyword(s):
Metadata
Show full item recordAbstract
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. Successful drug candidates necessarily represent a compromise between the numerous, sometimes competing objectives so that the benefits to patients outweigh potential drawbacks and risks. De novo drug design involves searching an immense space of feasible, druglike molecules to select those with the highest chances of becoming drugs using computational technology. Traditionally, de novo design has focused on designing molecules satisfying a single objective, such as similarity to a known ligand or an interaction score, and ignored the presence of the multiple objectives required for druglike behavior. Recently, methods have appeared in the literature that attempt to design molecules satisfying multiple predefined objectives and thereby produce candidate solutions with a higher chance of serving as viable drug leads. This paper describes the Multiobjective Evolutionary Graph Algorithm (MEGA), a new multiobjective optimization de novo design algorithmic framework that can be used to design structurally diverse molecules satisfying one or more objectives. The algorithm combines evolutionary techniques with graph-theory to directly manipulate graphs and perform an efficient global search for promising solutions. In the Experimental Section we present results from the application of MEGA for designing molecules that selectively bind to a known pharmaceutical target using the ChillScore interaction score family. The primary constraints applied to the design are based on the identified structure of the protein target and a known ligand currently marketed as a drug. A detailed explanation of the key elements of the specific implementation of the algorithm is given, including the methods for obtaining molecular building blocks, evolving the chemical graphs, and scoring the designed molecules. Our findings demonstrate that MEGA can produce structurally diverse candidate molecules representing a wide range of compromises of the supplied constraints and thus can be used as an "idea generator" to support expert chemists assigned with the task of molecular design. © 2009 American Chemical Society.
Collections
Cite as
Related items
Showing items related by title, author, creator and subject.
-
Conference Object
Optimal graph design using a knowledge-driven multi-objective evolutionary graph algorithm
Nicolaou, Christos A.; Kannas, Christos C.; Pattichis, Constantinos S. (2009)Designing appropriate graphs is a problem frequently occurring in several common applications ranging from designing communication and transportation networks to discovering new drugs. More often than not the graphs to be ...
-
Conference Object
A "plug-n-play" computationally efficient approach for control design of large-scale nonlinear systems using co-simulation
Baldi, S.; Michailidis, I.; Jula, H.; Kosmatopoulos, E. B.; Ioannou, Petros A. (Institute of Electrical and Electronics Engineers Inc., 2013)Recently, there has been a growing interest towards simulation-based control design (co-simulation), where the controller utilizes an optimizer to minimize or maximize an objective function (system performance) whose ...
-
Article
Design optimization and robotic fabrication of tensile mesh structures: The development and simulation of a custom-made end-effector tool
Kontovourkis, O.; Tryfonos, George (2016)This article presents an ongoing research, aiming to introduce a fabrication procedure for the development of tensile mesh systems. The purpose of current methodology is to establish an integrated approach that combines ...