Discovering Highly Potent Molecules from an Initial Set of Inactives Using Iterative Screening Article Swipe
Isidro Cortés‐Ciriano
,
Nicholas C. Firth
,
Andreas Bender
,
Oliver P Watson
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1021/acs.jcim.8b00376
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1021/acs.jcim.8b00376
The versatility of similarity searching and quantitative structure-activity relationships to model the activity of compound sets within given bioactivity ranges (i.e., interpolation) is well established. However, their relative performance in the common scenario in early stage drug discovery where lots of inactive data but no active data points are available (i.e., extrapolation from the low-activity to the high-activity range) has not been thoroughly examined yet. To this aim, we have designed an iterative virtual screening strategy which was evaluated on 25 diverse bioactivity data sets from ChEMBL. We benchmark the efficiency of random forest (RF), multiple linear regression, ridge regression, similarity searching, and random selection of compounds to identify a highly active molecule in the test set among a large number of low-potency compounds. We use the number of iterations required to find this active molecule to evaluate the performance of each experimental setup. We show that linear and ridge regression often outperform RF and similarity searching, reducing the number of iterations to find an active compound by a factor of 2 or more. Even simple regression methods seem better able to extrapolate to high-bioactivity ranges than RF, which only provides output values in the range covered by the training set. In addition, examination of the scaffold diversity in the data sets used shows that in some cases similarity searching and RF require two times as many iterations as random selection depending on the chemical space covered in the initial training data. Lastly, we show using bioactivity data for COX-1 and COX-2 that our framework can be extended to multitarget drug discovery, where compounds are selected by concomitantly considering their activity against multiple targets. Overall, this study provides an approach for iterative screening where only inactive data are present in early stages of drug discovery in order to discover highly potent compounds and the best experimental set up in which to do so.
Related Topics
Concepts
chEMBL
Random forest
Virtual screening
Computer science
Similarity (geometry)
Chemical space
Benchmark (surveying)
Set (abstract data type)
Regression
Extrapolation
Linear regression
Range (aeronautics)
Data mining
Mathematics
Drug discovery
Artificial intelligence
Machine learning
Statistics
Bioinformatics
Biology
Image (mathematics)
Geography
Materials science
Programming language
Geodesy
Composite material
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1021/acs.jcim.8b00376
- OA Status
- green
- Cited By
- 42
- References
- 84
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2888526648
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2888526648Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1021/acs.jcim.8b00376Digital Object Identifier
- Title
-
Discovering Highly Potent Molecules from an Initial Set of Inactives Using Iterative ScreeningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2018Year of publication
- Publication date
-
2018-08-21Full publication date if available
- Authors
-
Isidro Cortés‐Ciriano, Nicholas C. Firth, Andreas Bender, Oliver P WatsonList of authors in order
- Landing page
-
https://doi.org/10.1021/acs.jcim.8b00376Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://zenodo.org/record/3497816Direct OA link when available
- Concepts
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chEMBL, Random forest, Virtual screening, Computer science, Similarity (geometry), Chemical space, Benchmark (surveying), Set (abstract data type), Regression, Extrapolation, Linear regression, Range (aeronautics), Data mining, Mathematics, Drug discovery, Artificial intelligence, Machine learning, Statistics, Bioinformatics, Biology, Image (mathematics), Geography, Materials science, Programming language, Geodesy, Composite materialTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
42Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 5, 2023: 5, 2022: 2, 2021: 6Per-year citation counts (last 5 years)
- References (count)
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84Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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