ArticleFrontiers in pharmacology2022
Limits of Prediction for Machine Learning in Drug Discovery.
Article in Frontiers in pharmacology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- A Combined Chemoinformatics- and Machine Learning-Based Approach Identifies Chlormidazole as a Drug Repurposing Candidate against Aggressive Prostate Cancer.Journal of medicinal chemistry · 2026Article
- Artificial Intelligence (AI) in Pharmaceutical Formulation and Dosage Calculations.Pharmaceutics · 2025Review
- Discovery of Highly Potent BET Inhibitors based on a Tractable Tricyclic Scaffold.ACS medicinal chemistry letters · 2025Article
- Directional ΔJournal of chemical information and modeling · 2024Article
- Editorial: Microfluidics and mass spectrometry in drug discovery and development: from synthesis to evaluation.Frontiers in pharmacology · 2023Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In drug discovery, molecules are optimized towards desired properties. In this context, machine learning is used for extrapolation in drug discovery projects. The limits of extrapolation for regression models are known. However, a systematic analysis of the effectiveness of extrapolation in drug discovery has not yet been performed. In response, this study examined the capabilities of six machine learning algorithms to extrapolate from 243 datasets. The response values calculated from the molecules in the datasets were molecular weight, cLogP, and the number of sp3-atoms. Three experimental set ups were chosen for response values. Shuffled data were used for interpolation, whereas data for extrapolation were sorted from high to low values, and the reverse. Extrapolation with sorted data resulted in much larger prediction errors than extrapolation with shuffled data. Additionally, this study demonstrated that linear machine learning methods are preferable for extrapolation.
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Registered trials
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