Presentation held at:
3rd Joint Sheffield Conference on Chemoinformatics
April 2004, Sheffield, United Kingdom
The de novo approach to rational drug design offers a powerful tool to suggest entirely novel potential leads in cases where other drug design methods fail.
However programs for structure generation typically produce large numbers of putative ligands, therefore various heuristics (such as estimation of synthetic accessibility and binding affinity) have to be adopted to evaluate and prune large answer sets with the goal of suggesting ligands with high affinity but low structural complexity. We have developed a method for complexity analysis which provides a fast and effective ranking technique for elimination of structures with unfeasible molecular motifs.
The complexity analysis technique, implemented for the SPROUT de novo design system, is based on the statistical distribution of various cyclic and chain structural motifs and substitution patterns in existing drugs. The complexity score for a putative ligand is calculated by matching structural features against those of compounds in a database of drug molecules.
The novel feature of our technique that distinguishes it from other published methods is that the matching takes place at various levels of abstraction, so that it can evaluate complexity scores for partially substituted structures which is the case in SPROUT where molecular structures are built in stepwise fashion utilising a small library of generic and specific building blocks.
The presentation will include examples of applications of the technique to problems in structure based drug design.