---
title: "Machine-learning Potency Prediction and Multisource Target Evidence for Alzheimer’s Drug Discovery"
id: "biorxiv-18-integrating-multisource-target-evidence-with-machine-learning-potency"
canonical_url: "https://medichelpline.com/clinical-feed/biorxiv-18-integrating-multisource-target-evidence-with-machine-learning-potency"
content_type: "clinical_feed_article"
specialty: "Neurology"
source_name: "bioRxiv (Biomedical Preprints)"
source_url: "https://www.biorxiv.org/content/10.64898/2026.10.14.759026v1?rss=1"
published_at: "2026-10-20T12:00:00.000Z"
evidence_level: "Verified Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Machine-learning Potency Prediction and Multisource Target Evidence for Alzheimer’s Drug Discovery
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/biorxiv-18-integrating-multisource-target-evidence-with-machine-learning-potency
- **Specialty:** [Neurology](https://medichelpline.com/clinical-feed/neurology.md)
- **Primary Source:** bioRxiv (Biomedical Preprints)
- **Source URL:** [Original Journal Publication](https://www.biorxiv.org/content/10.64898/2026.10.14.759026v1?rss=1)
- **Published At:** 2026-10-20T12:00:00.000Z
- **Evidence Rating:** Verified Feed
## Executive GIST (TL;DR)
- The authors integrated multisource Alzheimer's disease (**AD**) target evidence into a traceable platform built around AGORA-centered data and three workspaces: Target Explorer, Drug-Target Lookup, and Model Portfolio. - The resource aggregated 967 targets, 452,249 compounds, and 1,430,260 measurements drawn from biological, chemical, structural, regulatory, and trial-registry sources; the platform separates contextual evidence from therapeutic claims. - On a curated **PIK3CA** dataset, fine-tuning a Boltz-2 affinity head with target-specific data raised Pearson r from 0.248 to 0.560 and lowered mean absolute error from 0.900 to 0.706 on 469 held-out compounds, investigators reported. - The team benchmarked 42 regression models across 50 random splits on ten moderate-sized target datasets; a random forest model achieved the highest mean R^2 for **XDH** (reported as 0.815). - The platform provides model-ready datasets, reproducible evaluation workflows, and public results while explicitly distinguishing random-split performance from generalization to unseen chemical scaffolds. - The article is a preprint; the authors note no competing interests and provide links to data/code and the AGORA resource. - The source did not report details on experimental assay protocols, chemical scaffold-split performance metrics beyond the statements above, or any regulatory milestones; those items were not described in the preprint.
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Anyu Pan University of Pennsylvania * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Anyu%2BPan%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Pan%20A&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AAnyu%2BPan%2B) * [ORCID record for Anyu Pan](http://orcid.org/0009-0009-5926-8240 "Open in new tab") Tianhua Zhai University of Pennsylvania * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Tianhua%2BZhai%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Zhai%20T&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3ATianhua%2BZhai%2B) * [ORCID record for Tianhua Zhai](http://orcid.org/0009-0007-9712-9132 "Open in new tab") Jonghyun Lee University of Pennsylvania * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Jonghyun%2BLee%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Lee%20J&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AJonghyun%2BLee%2B) Charles Zheng University of Pennsylvania * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Charles%2BZheng%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Zheng%20C&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3ACharles%2BZheng%2B) Dokyoon Kim University of Pennsylvania * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Dokyoon%2BKim%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Kim%20D&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3ADokyoon%2BKim%2B) Li Shen University of Pennsylvania * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Li%2BShen%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Shen%20L&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3ALi%2BShen%2B) * [ORCID record for Li Shen](http://orcid.org/0000-0002-5443-0503 "Open in new tab") * For correspondence: li.shen@pennmedicine.upenn.edu * [Abstract](https://www.biorxiv.org/content/10.64898/2026.10.14.759026v1)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_art/node:5885423/1) * [Info/History](https://www.biorxiv.org/content/10.64898/2026.10.14.759026v1.article-info)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_info/node:5885423/1) * [Metrics](https://www.biorxiv.org/content/10.64898/2026.10.14.759026v1.article-metrics)[](https://www.biorxiv.org/panels_ajax_tab/article_tab_metrics/node:5885423/1) * [Data/Code](https://www.biorxiv.org/content/10.64898/2026.10.14.759026v1.external-links)[](https://www.biorxiv.org/panels_ajax_tab/article_tab_data_code/node:5885423/1) * [ Preview PDF](https://www.biorxiv.org/content/10.64898/2026.10.14.759026v1.full.pdf+html)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_pdf/node:5885423/1) ![Loading](https://www.biorxiv.org/sites/all/modules/contrib/panels_ajax_tab/images/loading.gif) ## Abstract Alzheimer's disease (AD) target evidence is distributed across biological, chemical, structural, regulatory, and trial-registry sources, complicating reproducible target evaluation and modeling. We developed a traceable data-to-model platform that integrates AGORA-centered evidence through Target Explorer, Drug-Target Lookup, and Model Portfolio workspaces. The resource contains 967 targets, 452,249 compounds, and 1,430,260 measurements. On a curated PIK3CA dataset, target-specific fine-tuning of the Boltz-2 affinity head increased Pearson r from 0.248 to 0.560 and reduced mean absolute error from 0.900 to 0.706 on 469 held-out compounds. Separately, we screened 42 regression models over 50 random splits for ten moderate-sized target datasets; a random forest model for XDH achieved the highest mean R^2 (0.815). The platform connects curated evidence, model-ready datasets, reproducible evaluation, and public results while clearly separating contextual evidence from therapeutic claims and random-split performance from generalization to unseen chemical scaffolds. ### Competing Interest Statement The authors have declared no competing interest. ## Footnotes * ## Funder Information Declared National Institutes of Health (NIH), R01 AG071470, U01 AG068057, U01 AG066833 Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. 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. [Donate to openRxiv ](https://www.zeffy.com/en-US/donation-form/donate-to-make-a-difference-10981) [ Back to top](https://www.biorxiv.org/content/10.64898/2026.10.14.759026v1?rss=1#page) [ Previous](https://www.biorxiv.org/content/10.64898/2026.10.13.758990v1 "Females do not avoid but prefer superparasitism in the parasitoid wasp Melittobia australica ") Posted October 20, 2026. 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