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To address this limitation, we present MHNfs, an application specifically designed to predict molecular activity in low-data scenarios. At its core, MHNfs leverages a state-of-the-art few-shot activity prediction model, named MHNfs, which has demonstrated strong performance across a large set of prediction tasks in the benchmark data set FS-Mol. The application features an intuitive interface that enables users to prompt the model for precise activity predictions based on a small number of known active and inactive molecules, akin to interactive interfaces for large language models. To evaluate its efficacy, we simulate real-world scenarios by recasting PubChem bioassays as few-shot prediction tasks. MHNfs offers a streamlined and accessible solution for deploying advanced few-shot learning models, providing a valuable tool for accelerating drug discovery.<\/jats:p>","DOI":"10.1021\/acs.jcim.4c02373","type":"journal-article","created":{"date-parts":[[2025,4,30]],"date-time":"2025-04-30T04:48:56Z","timestamp":1745988536000},"page":"4243-4250","source":"Crossref","is-referenced-by-count":5,"title":["MHNfs: Prompting In-Context Bioactivity Predictions\nfor Low-Data Drug Discovery"],"prefix":"10.1021","volume":"65","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1372-8290","authenticated-orcid":true,"given":"Johannes","family":"Schimunek","sequence":"first","affiliation":[{"name":"Johannes Kepler University Linz , , ,","place":["Linz, Austria, A-4040"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9387-1427","authenticated-orcid":true,"given":"Sohvi","family":"Luukkonen","sequence":"additional","affiliation":[{"name":"Johannes Kepler University Linz , , ,","place":["Linz, Austria, A-4040"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2861-5552","authenticated-orcid":true,"given":"Gu\u0308nter","family":"Klambauer","sequence":"additional","affiliation":[{"name":"Johannes Kepler University Linz , , ,","place":["Linz, Austria, A-4040"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2025,4,30]]},"reference":[{"key":"2026090711195988200_cit1","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1145\/3287560.3287596","article-title":"Model cards for model reporting","volume-title":"Proceedings of the conference on fairness, accountability,\nand transparency","author":"Mitchell","year":"2019"},{"key":"2026090711195988200_cit2","first-page":"81","volume-title":"Machine learning in\nchemistry: data-driven algorithms, learning systems, and predictions","author":"Green","year":"2019"},{"key":"2026090711195988200_cit3","doi-asserted-by":"publisher","first-page":"709","DOI":"10.1007\/s10822-020-00317-x","article-title":"Artificial intelligence in chemistry and drug design","volume":"34","author":"Brown","year":"2020","journal-title":"Journal of Computer-Aided Molecular Design"},{"key":"2026090711195988200_cit4","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/B978-0-12-822249-2.00002-5","volume-title":"Computational and data-driven chemistry\nusing artificial intelligence","author":"Tyrchan","year":"2022"},{"key":"2026090711195988200_cit5","doi-asserted-by":"publisher","first-page":"100056","DOI":"10.1016\/j.ailsci.2022.100056","article-title":"Machine learning\nfor small molecule drug discovery\nin academia and industry","volume":"3","author":"Volkamer","year":"2023","journal-title":"Artificial Intelligence\nin the Life Sciences"},{"key":"2026090711195988200_cit6","doi-asserted-by":"publisher","first-page":"877","DOI":"10.1038\/nrd4507","article-title":"New drugs\ncost US 2.6 billion to develop","volume":"13","author":"Mullard","year":"2014","journal-title":"Nat. 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