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Clemons PA, Bittker JA, Wagner FF, Hands A, Dančík V, Schreiber SL, Choudhary A, Wagner BK. The Use of Informer Sets in Screening: Perspectives on an Efficient Strategy to Identify New Probes. SLAS DISCOVERY 2021; 26:855-861. [PMID: 34130532 DOI: 10.1177/24725552211019410] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Small-molecule discovery typically involves large-scale screening campaigns, spanning multiple compound collections. However, such activities can be cost- or time-prohibitive, especially when using complex assay systems, limiting the number of compounds tested. Further, low hit rates can make the process inefficient. Sparse coverage of chemical structure or biological activity space can lead to limited success in a primary screen and represents a missed opportunity by virtue of selecting the "wrong" compounds to test. Thus, the choice of screening collections becomes of paramount importance. In this perspective, we discuss the utility of generating "informer sets" for small-molecule discovery, and how this strategy can be leveraged to prioritize probe candidates. While many researchers may assume that informer sets are focused on particular targets (e.g., kinases) or processes (e.g., autophagy), efforts to assemble informer sets based on historical bioactivity or successful human exposure (e.g., repurposing collections) have shown promise as well. Another method for generating informer sets is based on chemical structure, particularly when the compounds have unknown activities and targets. We describe our efforts to screen an informer set representing a collection of 100,000 small molecules synthesized through diversity-oriented synthesis (DOS). This process enables researchers to identify activity early and more extensively screen only a few chemical scaffolds, rather than the entire collection. This elegant and economic outcome is a goal of the informer set approach. Here, we aim not only to shed light on this process, but also to promote the use of informer sets more widely in small-molecule discovery projects.
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Affiliation(s)
- Paul A Clemons
- Chemical Biology and Therapeutics Science Program, Broad Institute, Cambridge, MA, USA
| | - Joshua A Bittker
- Center for the Development of Therapeutics, Broad Institute, Cambridge, MA, USA.,Vertex Pharmaceuticals, Boston, MA, USA
| | - Florence F Wagner
- Center for the Development of Therapeutics, Broad Institute, Cambridge, MA, USA
| | - Allison Hands
- Center for the Development of Therapeutics, Broad Institute, Cambridge, MA, USA
| | - Vlado Dančík
- Chemical Biology and Therapeutics Science Program, Broad Institute, Cambridge, MA, USA
| | - Stuart L Schreiber
- Chemical Biology and Therapeutics Science Program, Broad Institute, Cambridge, MA, USA
| | - Amit Choudhary
- Chemical Biology and Therapeutics Science Program, Broad Institute, Cambridge, MA, USA
| | - Bridget K Wagner
- Chemical Biology and Therapeutics Science Program, Broad Institute, Cambridge, MA, USA
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6
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Buendia R, Kogej T, Engkvist O, Carlsson L, Linusson H, Johansson U, Toccaceli P, Ahlberg E. Accurate Hit Estimation for Iterative Screening Using Venn-ABERS Predictors. J Chem Inf Model 2019; 59:1230-1237. [PMID: 30726080 DOI: 10.1021/acs.jcim.8b00724] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Iterative screening has emerged as a promising approach to increase the efficiency of high-throughput screening (HTS) campaigns in drug discovery. By learning from a subset of the compound library, inferences on what compounds to screen next can be made by predictive models. One of the challenges of iterative screening is to decide how many iterations to perform. This is mainly related to difficulties in estimating the prospective hit rate in any given iteration. In this article, a novel method based on Venn-ABERS predictors is proposed. The method provides accurate estimates of the number of hits retrieved in any given iteration during an HTS campaign. The estimates provide the necessary information to support the decision on the number of iterations needed to maximize the screening outcome. Thus, this method offers a prospective screening strategy for early-stage drug discovery.
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Affiliation(s)
- Ruben Buendia
- Department of Information Technology , University of Borås , SE-501 90 Borås , Sweden
| | - Thierry Kogej
- Discovery Sciences , AstraZeneca IMED Biotech Unit , SE-431 83 Mölndal , Sweden
| | - Ola Engkvist
- Discovery Sciences , AstraZeneca IMED Biotech Unit , SE-431 83 Mölndal , Sweden
| | - Lars Carlsson
- Discovery Sciences , AstraZeneca IMED Biotech Unit , SE-431 83 Mölndal , Sweden.,Department of Computer Science, Royal Holloway , University of London , Egham , Surrey TW20 0EX , United Kingdom
| | - Henrik Linusson
- Department of Information Technology , University of Borås , SE-501 90 Borås , Sweden
| | - Ulf Johansson
- Department of Information Technology , University of Borås , SE-501 90 Borås , Sweden
| | - Paolo Toccaceli
- Department of Computer Science, Royal Holloway , University of London , Egham , Surrey TW20 0EX , United Kingdom
| | - Ernst Ahlberg
- Data Science and AI, Drug Safety & Metabolism , AstraZeneca IMED Biotech Unit , SE-431 83 Mölndal , Sweden
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7
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Sturm N, Sun J, Vandriessche Y, Mayr A, Klambauer G, Carlsson L, Engkvist O, Chen H. Application of Bioactivity Profile-Based Fingerprints for Building Machine Learning Models. J Chem Inf Model 2018; 59:962-972. [DOI: 10.1021/acs.jcim.8b00550] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Affiliation(s)
- Noé Sturm
- Hit Discovery, Discovery Sciences, IMED Biotech Unit, AstraZeneca, Pepparedsleden 1, 43153 Mölndal, Sweden
| | - Jiangming Sun
- Hit Discovery, Discovery Sciences, IMED Biotech Unit, AstraZeneca, Pepparedsleden 1, 43153 Mölndal, Sweden
| | - Yves Vandriessche
- Intel Corporation, Data Center Group, Veldkant 31, 2550 Kontich, Belgium
| | - Andreas Mayr
- LIT AI Lab & Institute for Machine Learning, Johannes Kepler University Linz, Altenbergerstr 69, 4040 Linz, Austria
| | - Günter Klambauer
- LIT AI Lab & Institute for Machine Learning, Johannes Kepler University Linz, Altenbergerstr 69, 4040 Linz, Austria
| | - Lars Carlsson
- Quantitative Biology, Discovery Sciences, IMED Biotech Unit, AstraZeneca, Pepparedsleden 1, 43153 Mölndal, Sweden
| | - Ola Engkvist
- Hit Discovery, Discovery Sciences, IMED Biotech Unit, AstraZeneca, Pepparedsleden 1, 43153 Mölndal, Sweden
| | - Hongming Chen
- Hit Discovery, Discovery Sciences, IMED Biotech Unit, AstraZeneca, Pepparedsleden 1, 43153 Mölndal, Sweden
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8
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Paricharak S, Méndez-Lucio O, Chavan Ravindranath A, Bender A, IJzerman AP, van Westen GJP. Data-driven approaches used for compound library design, hit triage and bioactivity modeling in high-throughput screening. Brief Bioinform 2018; 19:277-285. [PMID: 27789427 PMCID: PMC6018726 DOI: 10.1093/bib/bbw105] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2016] [Revised: 09/26/2016] [Indexed: 12/25/2022] Open
Abstract
High-throughput screening (HTS) campaigns are routinely performed in pharmaceutical companies to explore activity profiles of chemical libraries for the identification of promising candidates for further investigation. With the aim of improving hit rates in these campaigns, data-driven approaches have been used to design relevant compound screening collections, enable effective hit triage and perform activity modeling for compound prioritization. Remarkable progress has been made in the activity modeling area since the recent introduction of large-scale bioactivity-based compound similarity metrics. This is evidenced by increased hit rates in iterative screening strategies and novel insights into compound mode of action obtained through activity modeling. Here, we provide an overview of the developments in data-driven approaches, elaborate on novel activity modeling techniques and screening paradigms explored and outline their significance in HTS.
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Affiliation(s)
- Shardul Paricharak
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, United Kingdom
- Division of Medicinal Chemistry, Leiden Academic Centre for Drug Research, Leiden University, RA Leiden, The Netherlands
| | - Oscar Méndez-Lucio
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, United Kingdom
- Facultad de Química, Departamento de Farmacia, Universidad Nacional Autónoma de México, Avenida Universidad 3000, Mexico City, Mexico
| | - Aakash Chavan Ravindranath
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, United Kingdom
| | - Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, United Kingdom
| | - Adriaan P IJzerman
- Division of Medicinal Chemistry, Leiden Academic Centre for Drug Research, Leiden University, RA Leiden, The Netherlands
| | - Gerard J P van Westen
- Division of Medicinal Chemistry, Leiden Academic Centre for Drug Research, Leiden University, RA Leiden, The Netherlands
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10
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Pertusi DA, O’Donnell G, Homsher MF, Solly K, Patel A, Stahler SL, Riley D, Finley MF, Finger EN, Adam GC, Meng J, Bell DJ, Zuck PD, Hudak EM, Weber MJ, Nothstein JE, Locco L, Quinn C, Amoss A, Squadroni B, Hartnett M, Heo MR, White T, May SA, Boots E, Roberts K, Cocchiarella P, Wolicki A, Kreamer A, Kutchukian PS, Wassermann AM, Uebele VN, Glick M, Rusinko A, Culberson JC. Prospective Assessment of Virtual Screening Heuristics Derived Using a Novel Fusion Score. SLAS DISCOVERY 2017; 22:995-1006. [DOI: 10.1177/2472555217706058] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
High-throughput screening (HTS) is a widespread method in early drug discovery for identifying promising chemical matter that modulates a target or phenotype of interest. Because HTS campaigns involve screening millions of compounds, it is often desirable to initiate screening with a subset of the full collection. Subsequently, virtual screening methods prioritize likely active compounds in the remaining collection in an iterative process. With this approach, orthogonal virtual screening methods are often applied, necessitating the prioritization of hits from different approaches. Here, we introduce a novel method of fusing these prioritizations and benchmark it prospectively on 17 screening campaigns using virtual screening methods in three descriptor spaces. We found that the fusion approach retrieves 15% to 65% more active chemical series than any single machine-learning method and that appropriately weighting contributions of similarity and machine-learning scoring techniques can increase enrichment by 1% to 19%. We also use fusion scoring to evaluate the tradeoff between screening more chemical matter initially in lieu of replicate samples to prevent false-positives and find that the former option leads to the retrieval of more active chemical series. These results represent guidelines that can increase the rate of identification of promising active compounds in future iterative screens.
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Affiliation(s)
- Dante A. Pertusi
- Modeling and Informatics, Merck & Co., Inc., West Point, PA, USA
| | - Gregory O’Donnell
- Screening and Protein Sciences, Merck & Co., Inc., North Wales, PA, USA
- Merck & Co., Inc., West Point, PA, USA
| | - Michelle F. Homsher
- Screening and Protein Sciences, Merck & Co., Inc., North Wales, PA, USA
- Merck & Co., Inc., West Point, PA, USA
| | - Kelli Solly
- Screening and Protein Sciences, Merck & Co., Inc., North Wales, PA, USA
- Merck & Co., Inc., West Point, PA, USA
| | - Amita Patel
- Screening and Protein Sciences, Merck & Co., Inc., North Wales, PA, USA
- Merck & Co., Inc., West Point, PA, USA
| | - Shannon L. Stahler
- Screening and Protein Sciences, Merck & Co., Inc., North Wales, PA, USA
- Merck & Co., Inc., West Point, PA, USA
| | - Daniel Riley
- Screening and Protein Sciences, Merck & Co., Inc., North Wales, PA, USA
- Merck & Co., Inc., West Point, PA, USA
| | - Michael F. Finley
- Screening and Protein Sciences, Merck & Co., Inc., North Wales, PA, USA
- Discovery Sciences, Janssen Research and Development LLC, Spring House, PA, USA
| | - Eleftheria N. Finger
- Screening and Protein Sciences, Merck & Co., Inc., North Wales, PA, USA
- Discovery & Preclinical Development, GlaxoSmithKline, Collegeville, PA, USA
| | - Gregory C. Adam
- Screening and Protein Sciences, Merck & Co., Inc., North Wales, PA, USA
- Merck & Co., Inc., West Point, PA, USA
| | - Juncai Meng
- Screening and Protein Sciences, Merck & Co., Inc., North Wales, PA, USA
| | - David J. Bell
- Screening and Protein Sciences, Merck & Co., Inc., North Wales, PA, USA
- Merck & Co., Inc., North Wales, PA, USA
| | - Paul D. Zuck
- Merck & Co., Inc., North Wales, PA, USA
- Automation and Engineering, Merck & Co., Inc., North Wales, PA, USA
| | - Edward M. Hudak
- Discovery Sample Management, Merck & Co., Inc., North Wales, PA, USA
| | - Michael J. Weber
- Automation and Engineering, Merck & Co., Inc., North Wales, PA, USA
| | - Jennifer E. Nothstein
- Merck & Co., Inc., West Point, PA, USA
- Automation and Engineering, Merck & Co., Inc., North Wales, PA, USA
| | - Louis Locco
- Automation and Engineering, Merck & Co., Inc., North Wales, PA, USA
| | - Carissa Quinn
- Discovery Sciences, Janssen Research and Development LLC, Spring House, PA, USA
- Automation and Engineering, Merck & Co., Inc., North Wales, PA, USA
| | - Adam Amoss
- Automation and Engineering, Merck & Co., Inc., North Wales, PA, USA
| | - Brian Squadroni
- Merck & Co., Inc., West Point, PA, USA
- Automation and Engineering, Merck & Co., Inc., North Wales, PA, USA
| | - Michelle Hartnett
- Discovery Sciences, Janssen Research and Development LLC, Spring House, PA, USA
- Automation and Engineering, Merck & Co., Inc., North Wales, PA, USA
| | - Mee Ra Heo
- Screening and Protein Sciences, Merck & Co., Inc., North Wales, PA, USA
- Merck & Co., Inc., North Wales, PA, USA
| | - Tara White
- Discovery Sample Management, Merck & Co., Inc., North Wales, PA, USA
| | - S. Alex May
- Automation and Engineering, Merck & Co., Inc., North Wales, PA, USA
| | - Evelyn Boots
- Screening and Protein Sciences, Merck & Co., Inc., North Wales, PA, USA
| | - Kenneth Roberts
- Automation and Engineering, Merck & Co., Inc., North Wales, PA, USA
| | | | - Alex Wolicki
- Screening and Protein Sciences, Merck & Co., Inc., North Wales, PA, USA
| | - Anthony Kreamer
- Screening and Protein Sciences, Merck & Co., Inc., North Wales, PA, USA
- Merck & Co., Inc., Kenilworth, NJ, USA
| | | | | | - Victor N. Uebele
- Screening and Protein Sciences, Merck & Co., Inc., North Wales, PA, USA
- Merck & Co., Inc., North Wales, PA, USA
| | - Meir Glick
- Modeling and Informatics, Merck & Co., Inc., Boston, MA, USA
| | - Andrew Rusinko
- Modeling and Informatics, Merck & Co., Inc., West Point, PA, USA
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