A Difficulty Information Approach to Substituent Selection in QSAR Studies

Borth, David M.; McKay, R. James; Elliott, J. Richard

doi:10.2307/1270466

Cited by 10 publications

(5 citation statements)

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“…This minimizes the determinant of the inverse and, thus, the prediction error for a regression model. Equivalently, information theory shows that this same criterion maximizes the expected entropy change, i.e., it selects the set of substituents that together carry the most information for estimating the model . Roughly speaking, maximizing the determinant requires large diagonal elements and small off-diagonals.…”

Section: Discussionmentioning

confidence: 99%

“…Other, more algorithmic methods are possible. Borth et al presented a technique to simultaneously optimize additional criteria, such as cost or synthetic difficulty along with the D-criterion for information content, such as for finding the most diverse design possible for a given price . Although this approach is rigorous and highly automated, the weights given to various criteria are still subjective, and the intuitive “what if” interaction with the design is absent.…”

Section: Discussionmentioning

confidence: 99%

“…Selecting a subset of experiments to best represent a much larger potential candidate set falls under the discipline of “experimental design”. Experimental design has been applied to many pharmaceutical problems, including the design of structure−activity relationship (SAR) compound sets, − the optimization of synthetic processes, , the design of calibration standards in analytical chemistry, and the selection of screening subsets from corporate chemical archives . D-optimal design has recently been applied to selecting small numbers of substituents from larger sets of suitable reagents to use in synthetic combinatorial libraries for drug discovery …”

Section: Experimental Designmentioning

confidence: 99%

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Beyond Mere Diversity: Tailoring Combinatorial Libraries for Drug Discovery

Martin¹,

Critchlow²

1998

J. Comb. Chem.

157

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Experimental Designmentioning

confidence: 99%

See 1 more Smart Citation

Beyond Mere Diversity: Tailoring Combinatorial Libraries for Drug Discovery

Martin¹,

Critchlow²

1998

J. Comb. Chem.

157

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“…There are also A, E, and G optimality which weight the edge points differently . Modifications to D-optimal edge designs can be made to accommodate difficulty or cost of synthesis, or a Bayesian modification to reduce dependency on the assumed model …”

Section: Methodsmentioning

confidence: 99%

Experimental Designs for Selecting Molecules from Large Chemical Databases

Higgs

Bemis

Watson

et al. 1997

J. Chem. Inf. Comput. Sci.

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Recent developments in high-throughput screening and combinatorial chemistry have generated interest in experimental design methods to select subsets of molecules from large chemical databases. In this manuscript three methods for selecting molecules from large databases are described: edge designs, spread designs, and coverage designs. Two algorithms with linear time complexity that approximate spread and coverage designs are described. These algorithms can be threaded for multiprocessor systems, are compatible with any definition of molecular distance, and may be applied to very large chemical databases. For example, ten thousand molecules were selected using the maximum dissimilarity approximation to a spread design from a sixty-dimensional simulated molecular database of one million molecules in approximately 6 h on a UNIX workstation.

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“…An alternative would be to add the bias directly to the diversity score function. Borth et al presented a technique to simultaneously optimize additional criteria, such as cost or synthetic difficulty, along with the D-criterion for information content, such as finding the most diverse design possible for a given price . We feel that assigning compounds to bins and selecting a quota from each bin is closer to the way practicing medicinal chemists and biologists think about drug discovery, and thus facilitates interaction within a project team.…”

Section: Methodsmentioning

confidence: 99%

Sensitivity Analysis and Other Improvements to Tailored Combinatorial Library Design

Martin¹,

Wong²

2000

J. Chem. Inf. Comput. Sci.

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"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.

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A Difficulty Information Approach to Substituent Selection in QSAR Studies

Cited by 10 publications

References 0 publications

Beyond Mere Diversity: Tailoring Combinatorial Libraries for Drug Discovery

Beyond Mere Diversity: Tailoring Combinatorial Libraries for Drug Discovery

Experimental Designs for Selecting Molecules from Large Chemical Databases

Sensitivity Analysis and Other Improvements to Tailored Combinatorial Library Design

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