Sensitivity analysis and other improvements to tailored combinatorial library design

"Tailoring" combinatorial libraries was developed several years ago as a very general and intuitive method to design diverse compound collections while controlling the profile of other pharmaceutically relevant properties. The candidate substituents were assigned to "categorical bins" according to their properties, and successive steps of D-optimal design were performed to generate diverse substituent sets consistent with required membership quotas from each bin. This serial algorithm was expedient to implement from existing D-optimal design codes, but was order-dependent and did not generally locate the very best possible design. A new "parallel" Fedorov search algorithm has now been implemented that can find the most diverse property-tailored design. An ambiguous mass penalty has been added, whereby most duplicate masses can be eliminated with little loss of library diversity. Sensitivity analysis has also been added to quantitatively explore the diversity trade-offs due to increasing or decreasing each specific kind of bias.

Beyond mere diversity: tailoring combinatorial libraries for drug discovery

Combinatorial library design attempts to choose the best set of substituents for a combinatorial synthetic scheme to maximize the chances of finding a useful compound, such as a drug lead. Initial efforts were focused primarily on maximizing diversity, perhaps allowing some bias by the inclusion of a small, fixed set of pharmacophoric substituents. However, many factors besides diversity impact good library design for drug discovery. A library can be better "tailored" by assigning the candidate substituents to categories such as polar, pharmacophoric, rigid, low molecular weight, and expensive. Stratified sampling by successive steps of D-optimal design generates diverse designs which are also consistent with desirable profiles of these properties. Comparing the diversity scores among design profiles reveals the tradeoffs between diversity, physical property distributions, synthetic difficulty, expense, and pharmacophoric bias. The diversity scores can be calibrated by scoring the best designs from subsets of the candidates made either from specific classes of substituents or by randomly eliminating candidates. This procedure shows how poor random designs are compared even to highly biased optimal designs. Library design requires a synergistic effort between computational and synthetic medicinal chemists, so specialized interactive software has been developed to integrate substructure searching, display, and statistical experimental design to facilitate this interaction for the effective design of well-tailored libraries.