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Machine learning (ML) methods have shown considerable promise in modeling structure\u2013activity relationships and compound potency. In this study, we present an integrated ML framework for predicting IC\n                    <jats:sub>50<\/jats:sub>\n                    and pIC\n                    <jats:sub>50<\/jats:sub>\n                    values of compounds active against SARS-CoV-2, key indicators of antiviral potency. The proposed framework comprises three complementary approaches: (i) a regression model for quantitative IC\n                    <jats:sub>50<\/jats:sub>\n                    prediction validated against experimental data; (ii) a classification model that categorizes compounds into active and inactive classes to support compound prioritization; and (iii) a multi-task neural network that jointly performs IC\n                    <jats:sub>50<\/jats:sub>\n                    regression and activity classification, enhancing predictive performance and interpretability. A distinctive feature of this work is the incorporation of ligand efficiency (LE) as a criterion for activity classification, offering an alternative perspective on compound prioritization that has not been previously explored in SARS-CoV-2 bioactivity modeling. The proposed models demonstrate strong predictive capability, achieving a coefficient of determination (\n                    <jats:inline-formula>\n                      <jats:tex-math>$$R^2$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    ) of 0.77 using a neural network with feature selection, while the Random Forest classifier attains an accuracy, precision, and recall of approximately 0.92. These results highlight the potential of integrated regression, classification, and multi-task learning approaches as scalable and cost-effective tools for SARS-CoV-2 bioactivity prediction and antiviral drug discovery.\n                  <\/jats:p>","DOI":"10.1186\/s12859-026-06573-2","type":"journal-article","created":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T10:29:17Z","timestamp":1785925757000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine learning-based prediction of SARS-CoV-2 bioactivity: integrating IC50 regression and activity classification using multi-task neural networks"],"prefix":"10.1186","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1503-0179","authenticated-orcid":false,"given":"Aya I.","family":"Maiyza","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sohila","family":"Osama","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanan A.","family":"Hassan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,5]]},"reference":[{"issue":"3","key":"6573_CR1","doi-asserted-by":"publisher","first-page":"1947","DOI":"10.1007\/s10462-021-10058-4","volume":"55","author":"S Dara","year":"2022","unstructured":"Dara S, Dhamercherla S, Jadav SS, Babu CM, Ahsan MJ. 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