Ha Sumin, Bang Dongmin, Kim Sun
Interdisciplinary Program in Artificial Intelligence, Seoul National University, Seoul, 08826, Republic of Korea.
Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 08826, Republic of Korea.
J Cheminform. 2025 May 14;17(1):74. doi: 10.1186/s13321-025-01012-5.
Toxicity is a critical hurdle in drug development, often causing the late-stage failure of promising compounds. Existing computational prediction models often focus on single-organ toxicity. However, avoiding toxicity of an organ, such as reducing gastrointestinal side effects, may inadvertently lead to toxicity in another organ, as seen in the real case of rofecoxib, which was withdrawn due to increased cardiovascular risks. Thus, simultaneous prediction of multi-organ toxicity is a desirable but challenging task. The main challenges are (1) the variability of substructures that contribute to toxicity of different organs, (2) insufficient power of molecular representations in diverse perspectives, and (3) explainability of prediction results especially in terms of substructures or potential toxicophores. To address these challenges with multiple strategies, we developed FATE-Tox, a novel multi-view deep learning framework for multi-organ toxicity prediction. For variability of substructures, we used three fragmentation methods such as BRICS, Bemis-Murcko scaffolds, and RDKit Functional Groups to formulate fragment-level graphs so that diverse substructures can be used to identify toxicity for different organs. For insufficient power of molecular representations, we used molecular representations in both 2D and 3D perspectives. For explainability, our fragment attention transformer identifies potential 3D toxicophores using attention coefficients. Scientific contribution: Our framework achieved significant improvements in prediction performance, with up to 3.01% gains over prior baseline methods on toxicity benchmark datasets from MoleculeNet (BBBP, SIDER, ClinTox) and TDC (DILI, Skin Reaction, Carcinogens, and hERG), while the multi-task learning approach further enhanced performance by up to 1.44% compared to the single-task learning framework that had already surpassed these baselines. Additionally, attention visualization aligning with literature contributes to greater transparency in predictive modeling. Our approach has the potential to provide scientists and clinicians with a more interpretable and clinically meaningful tool to assess systemic toxicity, ultimately supporting safer and more informed drug development processes.
毒性是药物研发中的一个关键障碍，常常导致有前景的化合物在后期研发失败。现有的计算预测模型通常聚焦于单一器官毒性。然而，避免某一器官的毒性，比如减少胃肠道副作用，可能会无意中导致另一器官出现毒性，就像罗非昔布的实际案例那样，它因心血管风险增加而被撤市。因此，同时预测多器官毒性是一项理想但具有挑战性的任务。主要挑战包括：（1）导致不同器官毒性的子结构的变异性；（2）从不同视角看分子表示能力不足；（3）预测结果的可解释性，特别是在子结构或潜在毒效基团方面。为了用多种策略应对这些挑战，我们开发了FATE-Tox，这是一种用于多器官毒性预测的新型多视图深度学习框架。对于子结构的变异性，我们使用了三种碎片化方法，如BRICS、Bemis-Murcko骨架和RDKit官能团，来构建片段级图，以便用不同的子结构识别不同器官的毒性。对于分子表示能力不足，我们从二维和三维视角使用分子表示。对于可解释性，我们的片段注意力变换器使用注意力系数识别潜在的三维毒效基团。科学贡献：我们的框架在预测性能上取得了显著提升，在来自MoleculeNet（BBBP、SIDER、ClinTox）和TDC（药物性肝损伤、皮肤反应、致癌物和人ether-a-go-go相关基因）的毒性基准数据集上，比先前的基线方法最多提高了3.01%，而与已经超越这些基线的单任务学习框架相比，多任务学习方法进一步将性能提高了1.44%。此外，与文献一致的注意力可视化有助于提高预测建模的透明度。我们的方法有潜力为科学家和临床医生提供一个更具可解释性和临床意义的工具来评估全身毒性，最终支持更安全、更明智的药物研发过程。
