Toxicity is paramount for comprehending compound properties, particularly in the early stages of drug design. Due to the diversity and complexity of toxic effects, it became a challenge to compute compound toxicity tasks. To address this issue, we propose a multimodal representation learning model, termed multimodal graph isomorphism network (MMGIN), to address this challenge for compound toxicity multitask learning. Based on fingerprints and molecular graphs of compounds, our MMGIN model incorporates a multimodal representation learning model to acquire a comprehensive compound representation. This model adopts a two-channel structure to independently learn fingerprint representation and molecular graph representation. Subsequently, two feedforward neural networks utilize the learned multimodal compound representation to perform multitask learning, encompassing compound toxicity classification and multiple compound category classification simultaneously. To test the effectiveness of our model, we constructed a novel data set, termed the compound toxicity multitask learning (CTMTL) data set, derived from the TOXRIC data set. We compare our MMGIN model with other representative machine learning and deep learning models on the CTMTL and Tox21 data sets. The experimental results demonstrate significant advancements achieved by our MMGIN model. Furthermore, the ablation study underscores the effectiveness of the introduced fingerprints, molecular graphs, the multimodal representation learning model, and the multitask learning model, showcasing the model’s superior predictive capability and robustness.

中文翻译：

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基于图同构网络的多模态表示学习用于毒性多任务学习


毒性对于理解化合物特性至关重要，尤其是在药物设计的早期阶段。由于毒性作用的多样性和复杂性，计算化合物毒性任务成为一项挑战。为了解决这个问题，我们提出了一种多模态表示学习模型，称为多模态图同构网络 （MMGIN），以应对化合物毒性多任务学习的这一挑战。基于化合物的指纹图谱和分子图，我们的 MMGIN 模型结合了多模态表示学习模型来获得全面的化合物表示。该模型采用双通道结构，独立学习指纹表示和分子图表示。随后，两个前馈神经网络利用学习到的多模态化合物表示来执行多任务学习，同时包括化合物毒性分类和多化合物类别分类。为了测试我们模型的有效性，我们构建了一个新的数据集，称为化合物毒性多任务学习 （CTMTL） 数据集，该数据集源自 TOXRIC 数据集。我们将 MMGIN 模型与 CTMTL 和 Tox21 数据集上的其他代表性机器学习和深度学习模型进行了比较。实验结果表明，我们的 MMGIN 模型取得了重大进步。此外，消融研究强调了引入的指纹、分子图、多模态表示学习模型和多任务学习模型的有效性，展示了该模型卓越的预测能力和稳健性。