---
title: "Uncertainty Benchmarking and Risk Ranking for Multi-task Bioactivity Prediction"
id: "biorxiv-12-benchmarking-uncertainty-and-improving-risk-ranking-in-multi-task-bioactivity"
canonical_url: "https://medichelpline.com/clinical-feed/biorxiv-12-benchmarking-uncertainty-and-improving-risk-ranking-in-multi-task-bioactivity"
content_type: "clinical_feed_article"
specialty: "Pharmacology"
source_name: "bioRxiv (Biomedical Preprints)"
source_url: "https://www.biorxiv.org/content/10.64898/2026.09.15.751772v1?rss=1"
published_at: "2026-09-22T12:00:00.000Z"
evidence_level: "Verified Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Uncertainty Benchmarking and Risk Ranking for Multi-task Bioactivity Prediction
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/biorxiv-12-benchmarking-uncertainty-and-improving-risk-ranking-in-multi-task-bioactivity
- **Specialty:** [Pharmacology](https://medichelpline.com/clinical-feed/pharmacology.md)
- **Primary Source:** bioRxiv (Biomedical Preprints)
- **Source URL:** [Original Journal Publication](https://www.biorxiv.org/content/10.64898/2026.09.15.751772v1?rss=1)
- **Published At:** 2026-09-22T12:00:00.000Z
- **Evidence Rating:** Verified Feed
## Executive GIST (TL;DR)
- The study evaluates uncertainty estimation and risk ranking performance for **multi-task bioactivity prediction** across a broad benchmark. - Five multi-task predictors were tested on 100 ChEMBL27 test assays and 100 ChEMBL37 assays under two data splits: **random split** and **clustering split**. - Uncertainty calibration methods compared included **native** uncertainty, **Deep Ensemble**, and **MC Dropout**. - Risk-ranking ability was assessed using two **Gaussian process** backbones and by measuring how well uncertainty orders predictions by error. - Adaptive deep kernel fitting (**ADKF**) produced the best mean calibration among tested approaches. - Deep kernel transfer (**DKT**) ranked prediction errors more effectively than ADKF when using native uncertainty. - The authors introduce Influence Calibrated Support Reconstruction (**ICSR**), a risk score for kernel-based predictors that combines support reconstruction error with query-specific influence and shrinks scores toward the full-support mean. - When applied with DKT and ADKF, **ICSR** improved all three mean risk-ranking metrics versus native uncertainty and an adapted neighborhood comparator across every panel and split. - ICSR yielded a mean half-query MAE reduction (R50) of 14.2–19.6%, where R50 quantifies the percentage decrease in MAE after retaining the lowest-risk half of queries. - A retrospective SARS-CoV-2 main protease case study illustrated using ICSR to select more reliable predictions. - The benchmark supports assessing both calibration and error ranking jointly, and ICSR is presented as a tool to improve selective use of kernel-based bioactivity predictions. - Specific implementation details, hyperparameters and full quantitative breakdowns beyond those summarized in the abstract were not reported in the source provided here.
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Zijian Wang Laboratory for Synthetic Chemistry and Chemical Biology Limited; State Key Laboratory of Synthetic Chemistry and Department of Chemistry, The University of Hong Kong * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Zijian%2BWang%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Wang%20Z&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AZijian%2BWang%2B) Li Tian Laboratory for Synthetic Chemistry and Chemical Biology Limited; State Key Laboratory of Synthetic Chemistry and Department of Chemistry, The University of Hong Kong * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Li%2BTian%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Tian%20L&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3ALi%2BTian%2B) * For correspondence: tianli@hksccb.hk Chi-Ming Che Laboratory for Synthetic Chemistry and Chemical Biology Limited; State Key Laboratory of Synthetic Chemistry and Department of Chemistry, The University of Hong Kong * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Chi-Ming%2BChe%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Che%20C&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AChi-Ming%2BChe%2B) * [Abstract](https://www.biorxiv.org/content/10.64898/2026.09.15.751772v1)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_art/node:5801206/1) * [Info/History](https://www.biorxiv.org/content/10.64898/2026.09.15.751772v1.article-info)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_info/node:5801206/1) * [Metrics](https://www.biorxiv.org/content/10.64898/2026.09.15.751772v1.article-metrics)[](https://www.biorxiv.org/panels_ajax_tab/article_tab_metrics/node:5801206/1) * [ Preview PDF](https://www.biorxiv.org/content/10.64898/2026.09.15.751772v1.full.pdf+html)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_pdf/node:5801206/1) ![Loading](https://www.biorxiv.org/sites/all/modules/contrib/panels_ajax_tab/images/loading.gif) ## Abstract Multi-task bioactivity prediction transfers information across assays, but experimental prioritization also requires uncertainty estimates that identify unreliable predictions. We benchmarked prediction accuracy and uncertainty calibration for five multi-task predictors on 100 ChEMBL27 test assays and 100 CHEMBL37 assays under the random split and the clustering split. Calibration used native, Deep Ensemble and MC Dropout uncertainty; risk ranking was compared for two Gaussian process (GP) backbones. Adaptive deep kernel fitting (ADKF) achieved the best mean calibration results, while deep kernel transfer (DKT) ranked errors more effectively than ADKF with native uncertainty. We also introduce Influence Calibrated Support Reconstruction (ICSR), a risk score for kernel-based predictors. ICSR combines measured support reconstruction errors with query-specific influence and stabilizes their weighted average toward the full-support mean to adjust Gaussian process uncertainty while preserving predicted activities. With DKT and ADKF, ICSR improved all three mean risk-ranking metrics over native uncertainty and an adapted neighborhood comparator in every panel and split. ICSR achieved a mean half-query MAE reduction (R50) of 14.2-19.6%, where R50 measures the percentage decrease in mean absolute error (MAE) after retaining the lowest-risk half of the queries. A retrospectively selected SARS-CoV-2 main protease case further illustrated its use for selecting more reliable predictions. The benchmark supports joint assessment of calibration and error ranking, while ICSR improves selective use of kernel-based bioactivity predictions. ### Competing Interest Statement The authors have declared no competing interest. ## Funder Information Declared Innovation and Technology Commission, https://ror.org/04vf9tr09 Laboratory for Synthetic Chemistry and Chemical Biology Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a [CC-BY-NC-ND 4.0 International license](http://creativecommons.org/licenses/by-nc-nd/4.0/). bioRxiv and medRxiv thank the following for their generous financial support: > The Chan Zuckerberg Initiative, Cold Spring Harbor Laboratory, the Sergey Brin Family Foundation, California Institute of Technology, Centre National de la Recherche Scientifique, Fred Hutchinson Cancer Center, Imperial College London, Massachusetts Institute of Technology, Stanford University, The University of Edinburgh, University of Washington, and Vrije Universiteit Amsterdam. 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