Background: Prioritizing building blocks for combinatorial medicinal chemistry represents an optimization task. We present the application of an artificial ant colony algorithm to combinatorial molecular design (Molecular Ant Algorithm [MAntA]).
Results: In a retrospective evaluation, the ant algorithm performed favorably compared with other stochastic optimization methods. Application of MAntA to peptide design resulted in new octapeptides exhibiting substantial binding to mouse MHC-I (H-2K(b)). In a second study, MAntA generated a new functional factor Xa inhibitor by Ugi-type three-component reaction.
Conclusion: This proof-of-concept study validates artificial ant systems as innovative computational tools for efficient building block prioritization in combinatorial chemistry. Focused activity-enriched compound collections are obtained without the need for exhaustive product enumeration.