ArticleJournal of cheminformatics2022
Tuning gradient boosting for imbalanced bioassay modelling with custom loss functions.
Article in Journal of cheminformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- From Algorithms to Assets: A Comprehensive Review of AI's Role in Preclinical Drug Discovery and the Hurdles to Clinical Translation.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Detecting Important Features and Predicting Yield from Defects Detected by SEM in Semiconductor Production.Sensors (Basel, Switzerland) · 2025Article
- SaGP: identifying plant saline-alkali tolerance genes based on machine learning techniques.Frontiers in plant science · 2025Article
- PET/CT-based 3D multi-class semantic segmentation of ovarian cancer and the stability of the extracted radiomics features.Physical and engineering sciences in medicine · 2024Article
- Machine Learning Assisted Hit Prioritization for High Throughput Screening in Drug Discovery.ACS central science · 2024Article
- Application of machine learning models on predicting the length of hospital stay in fragility fracture patients.BMC medical informatics and decision making · 2024Article
- Practical guidelines for the use of gradient boosting for molecular property prediction.Journal of cheminformatics · 2023Article
- Biomedical data analyses facilitated by open cheminformatics workflows.Journal of cheminformatics · 2023Article
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Authors and funding
4 authors.
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Abstract
While in the last years there has been a dramatic increase in the number of available bioassay datasets, many of them suffer from extremely imbalanced distribution between active and inactive compounds. Thus, there is an urgent need for novel approaches to tackle class imbalance in drug discovery. Inspired by recent advances in computer vision, we investigated a panel of alternative loss functions for imbalanced classification in the context of Gradient Boosting and benchmarked them on six datasets from public and proprietary sources, for a total of 42 tasks and 2 million compounds. Our findings show that with these modifications, we achieve statistically significant improvements over the conventional cross-entropy loss function on five out of six datasets. Furthermore, by employing these bespoke loss functions we are able to push Gradient Boosting to match or outperform a wide variety of previously reported classifiers and neural networks. We also investigate the impact of changing the loss function on training time and find that it increases convergence speed up to 8 times faster. As such, these results show that tuning the loss function for Gradient Boosting is a straightforward and computationally efficient method to achieve state-of-the-art performance on imbalanced bioassay datasets without compromising on interpretability and scalability.
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