Enhancing the rate of scaffold discovery with diversity-oriented prioritization

Swamidass, S. Joshua; Calhoun, Bradley T.; Bittker, Joshua A.; Bodycombe, Nicole E.; Clemons, Paul A.

doi:10.1093/bioinformatics/btr369

Cited by 9 publications

(15 citation statements)

References 27 publications

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“…The proof in 10 demonstrates that maximizing the EMD is a very good approximation for maximizing profit, as long as the very reasonable assumption holds that the utility function is an increasing function of discovery and a fixed cost model is used, as is the case in our framework.…”

Section: Methodsmentioning

confidence: 83%

“…Both models are trained using gradient descent on the cross-entropy error using the monotonic prior defined in. 10 This protocol yields models whose outputs are interpretable as probabilities, allowing us to define…”

Section: Methodsmentioning

confidence: 99%

“…15,16,17,18 Likewise, recent work in chemical informatics has suggested considering activity when picking diverse sets of molecules can be desirable. 19 In line with these efforts, this study extends the DOP method 10 by (1) introducing a more complex utility function which more accurately models many screeners’ preferences and (2) deriving a more general utility-aware prioritization algorithm which can accommodate several important screener preferences.…”

Section: Introductionmentioning

confidence: 98%

“…10 DOP works by formalizing a preference often stated by screeners: they are not looking for active molecules as much as looking for novel active scaffolds. 11,12 DOP defines a scaffold—or a particular cluster—as active if at least one example of the scaffold is successfully confirmed active and uses this definition to derive a utility-aware prioritization algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Utility-Aware Screening with Clique-Oriented Prioritization

Swamidass

Calhoun²,

Bittker

et al. 2011

J. Chem. Inf. Model.

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Most methods of deciding which hits from a screen to send for confirmatory testing assume that all confirmed actives are equally valuable and aim only to maximize the the number of confirmed hits. In contrast, “utility-aware” methods are informed by models of screeners’ preferences and can increase the rate at which the useful information is discovered. Clique-oriented prioritization (COP) extends a recently proposed economic framework and aims—by changing which hits are sent for confirmatory testing—to maximize the number of scaffolds with at least two confirmed active examples. In both retrospective and prospective experiments, COP enables accurate predictions of the number of clique discoveries in a batch of confirmatory experiments and improves the rate of clique discovery by more than three-fold. In contrast, other similarity-based methods like ontology-based pattern identification (OPI) and local hit-rate analysis (LHR) reduce the rate of scaffold discovery by about half. The utility-aware algorithm used to implement COP is general enough to implement several other important models of screener preferences.

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Section: Methodsmentioning

confidence: 83%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Utility-Aware Screening with Clique-Oriented Prioritization

Swamidass

Calhoun²,

Bittker

et al. 2011

J. Chem. Inf. Model.

Self Cite

View full text Add to dashboard Cite

show abstract

“…These methods include better strategies for identifying active molecules from primary screens, which leverages information from fingerprints [61], scaffold groupings [62, 63], economic modeling [64-66], and improved processing of raw data [67-69]. They also include automatic methods of organizing screening data into workflows [70] and a series of approaches for visualizing how biological activity maps to chemical space [71-74].…”

Section: Introductionmentioning

confidence: 99%

Bigger data, collaborative tools and the future of predictive drug discovery

Ekins

Clark²,

Swamidass

et al. 2014

J Comput Aided Mol Des

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Over the past decade we have seen a growth in the provision of chemistry data and cheminformatics tools as either free websites or software as a service (SaaS) commercial offerings. These have transformed how we find molecule-related data and use such tools in our research. There have also been efforts to improve collaboration between researchers either openly or through secure transactions using commercial tools. A major challenge in the future will be how such databases and software approaches handle larger amounts of data as it accumulates from high throughput screening and enables the user to draw insights, enable predictions and move projects forward. We now discuss how information from some drug discovery datasets can be made more accessible and how privacy of data should not overwhelm the desire to share it at an appropriate time with collaborators. We also discuss additional software tools that could be made available and provide our thoughts on the future of predictive drug discovery in this age of big data. We use some examples from our own research on neglected diseases, collaborations, mobile apps and algorithm development to illustrate these ideas.

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An informatic pipeline for managing high-throughput screening experiments and analyzing data from stereochemically diverse libraries

Mulrooney

Lahr

Quintin

et al. 2013

J Comput Aided Mol Des

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Integration of flexible data-analysis tools with cheminformatics methods is a prerequisite for successful identification and validation of “hits” in high-throughput screening (HTS) campaigns. We have designed, developed, and implemented a suite of robust yet flexible cheminformatics tools to support HTS activities at the Broad Institute, three of which are described herein. The “hit-calling” tool allows a researcher to set a hit threshold that can be varied during downstream analysis. The results from the hit-calling exercise are reported to a database for record keeping and further data analysis. The “cherry-picking” tool enables creation of an optimized list of hits for confirmatory and follow-up assays from an HTS hit list. This tool allows filtering by computed chemical property and by substructure. In addition, similarity searches can be performed on hits of interest and sets of related compounds can be selected. The third tool, an “S/SAR viewer,” has been designed specifically for the Broad Institute’s diversity-oriented synthesis (DOS) collection. The compounds in this collection are rich in chiral centers and the full complement of all possible stereoisomers of a given compound are present in the collection. The S/SAR viewer allows rapid identification of both structure/activity relationships and stereo-structure/activity relationships present in HTS data from the DOS collection. Together, these tools enable the prioritization and analysis of hits from diverse compound collections, and enable informed decisions for follow-up biology and chemistry efforts.

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Enhancing the rate of scaffold discovery with diversity-oriented prioritization

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 9 publications

References 27 publications

Utility-Aware Screening with Clique-Oriented Prioritization

Utility-Aware Screening with Clique-Oriented Prioritization

Bigger data, collaborative tools and the future of predictive drug discovery

An informatic pipeline for managing high-throughput screening experiments and analyzing data from stereochemically diverse libraries

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