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Maximizing gain in high-throughput screening using conformal prediction
Department of Chemistry, University of Cambridge, Cambridge, England; IOTA Pharmaceuticals, Cambridge, England.ORCID iD: 0000-0002-5556-8133
Department of Chemistry, University of Cambridge, Cambridge, England.
Unit of Toxicology Sciences, Karolinska Institute, Södertälje, Sweden; Department of Computer and Systems Sciences, Stockholm University, Kista, Sweden.ORCID iD: 0000-0003-3107-331X
Department of Chemistry, University of Cambridge, Cambridge, England.
2018 (English)In: Journal of Cheminformatics, E-ISSN 1758-2946, Vol. 10, no 1, article id 7Article in journal (Refereed) Published
Abstract [en]

Iterative screening has emerged as a promising approach to increase the efficiency of screening campaigns compared to traditional high throughput approaches. By learning from a subset of the compound library, inferences on what compounds to screen next can be made by predictive models, resulting in more efficient screening. One way to evaluate screening is to consider the cost of screening compared to the gain associated with finding an active compound. In this work, we introduce a conformal predictor coupled with a gain-cost function with the aim to maximise gain in iterative screening. Using this setup we were able to show that by evaluating the predictions on the training data, very accurate predictions on what settings will produce the highest gain on the test data can be made. We evaluate the approach on 12 bioactivity datasets from PubChem training the models using 20% of the data. Depending on the settings of the gain-cost function, the settings generating the maximum gain were accurately identified in 8-10 out of the 12 datasets. Broadly, our approach can predict what strategy generates the highest gain based on the results of the cost-gain evaluation: to screen the compounds predicted to be active, to screen all the remaining data, or not to screen any additional compounds. When the algorithm indicates that the predicted active compounds should be screened, our approach also indicates what confidence level to apply in order to maximize gain. Hence, our approach facilitates decision-making and allocation of the resources where they deliver the most value by indicating in advance the likely outcome of a screening campaign.

Place, publisher, year, edition, pages
London: Chemistry Central , 2018. Vol. 10, no 1, article id 7
Keywords [en]
Conformal prediction, HTS, Gain-cost function, PubChem datasets
National Category
Chemical Sciences Computer and Information Sciences
Identifiers
URN: urn:nbn:se:oru:diva-83143DOI: 10.1186/s13321-018-0260-4ISI: 000425976800001PubMedID: 29468427Scopus ID: 2-s2.0-85042426778OAI: oai:DiVA.org:oru-83143DiVA, id: diva2:1440448
Funder
Knut and Alice Wallenberg FoundationSwedish Research CouncilAstraZenecaAvailable from: 2018-04-06 Created: 2020-06-15 Last updated: 2022-05-10Bibliographically approved

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Norinder, Ulf

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