AI 中文总结
Finch整合多任务定量构效关系模型，解决传统混合物模型的局限，实现化学化合物及混合物的毒性剂量反应曲线预测，助力监管安全评估。
AI 中文摘要
化学混合物的整体研究方法正在重塑风险评估，其强调对混合物进行测试而非单一化合物测试，这既减少了动物测试的需求，也推动了建模方法的进步。大多数计算模型仍聚焦于单一化学物质，而浓度加和、独立作用等传统混合物模型存在局限性，它们难以应对多种作用模式，且常无法捕捉协同或拮抗效应。监管机构需要突破这些限制、更快速高效的模型。Finch通过一种新颖的工作流程解决了这些挑战：它将基于分子描述符的框架与深度学习嵌入整合至多任务定量构效关系模型中，以改进化学暴露预测。深度学习嵌入通过将关键特征提炼到潜在空间，保留了来自分子描述符、物理化学性质以及SMILES的大语言模型嵌入等多样输入的信息，从而增强了机器学习预测效果。Finch的多任务学习同时优化多个损失函数，利用所有可用数据学习通用表示，这使其能够有效建模混合物中复杂的成分相互作用，为监管安全评估提供了重大进展。
英文摘要
A holistic approach to chemical mixtures is reshaping risk assessment emphasizing mixture testing over single compounds eliminating animal testing and advancing modeling methods. Most computational models still focus on individual chemicals and conventional mixture models like concentration addition and independent action are limited they struggle with multiple Modes of Action and often miss synergistic or antagonistic effects. Regulatory agencies need faster more efficient models that go beyond these constraints. Finch addresses these challenges with a novel workflow. It integrates molecular descriptor based frameworks and deep learning embeddings in multi task quantitative structure activity relationship models for improved chemical exposure prediction. DL embeddings preserve information from diverse inputs molecular descriptors physicochemical properties and large language model embeddings from SMILES by distilling key features into a latent space enhancing machine learning predictions. Finch multi task learning optimizes multiple loss functions simultaneously leveraging all available data to learn generalized representations. This enables effective modeling of complex ingredient interactions within mixtures offering a significant advancement for regulatory safety assessment.
