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Current DILI datasets are characterized by small sizes and high imbalance, posing difficulties in learning robust representations and accurate modeling. To address these challenges, we trained a multi-modal multi-task model integrating preclinical histopathologies, biochemistry (blood markers), and clinical DILI-related adverse drug reactions (ADRs). Leveraging pretrained BERT models, we extracted representations covering a broad chemical space, facilitating robust learning in both frozen and fine-tuned settings. To address imbalanced data, we explored weighted Binary Cross-Entropy (w-BCE) and weighted Focal Loss (w-FL) . Our results demonstrate that the frozen BERT model consistently enhances performance across all metrics and modalities with weighted loss functions compared to their non-weighted counterparts. However, the efficacy of fine-tuning BERT varies across modalities, yielding inconclusive results. In summary, the incorporation of BERT features with weighted loss functions demonstrates advantages, while the efficacy of fine-tuning remains uncertain.<\/jats:p>","DOI":"10.1007\/978-3-031-72381-0_8","type":"book-chapter","created":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T13:10:16Z","timestamp":1726751416000},"page":"82-97","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Balancing Imbalanced Toxicity Models: Using MolBERT with\u00a0Focal Loss"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9190-3023","authenticated-orcid":false,"given":"Muhammad Arslan","family":"Masood","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1925-9154","authenticated-orcid":false,"given":"Samuel","family":"Kaski","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7059-4399","authenticated-orcid":false,"given":"Hugo","family":"Ceulemans","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7836-8989","authenticated-orcid":false,"given":"Dorota","family":"Herman","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7741-2279","authenticated-orcid":false,"given":"Markus","family":"Heinonen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,9,20]]},"reference":[{"unstructured":"Ahmad, W., Simon, E., Chithrananda, S., Grand, G. and Ramsundar, B.: ChemBERTa-2: Towards chemical foundation models. arXiv:2209.01712 (2022)","key":"8_CR1"},{"doi-asserted-by":"publisher","unstructured":"Ai, H., et al.: Predicting drug-induced liver injury using ensemble learning methods and molecular fingerprints. 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