Viganò Edoardo Luca, Ballabio Davide, Roncaglioni Alessandra
Laboratory of Environmental Toxicology and Chemistry, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCSS, 20156 Milan, Italy.
Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, 20126 Milan, Italy.
Toxics. 2024 Jan 19;12(1):87. doi: 10.3390/toxics12010087.
Cardiovascular disease is a leading global cause of mortality. The potential cardiotoxic effects of chemicals from different classes, such as environmental contaminants, pesticides, and drugs can significantly contribute to effects on health. The same chemical can induce cardiotoxicity in different ways, following various Adverse Outcome Pathways (AOPs). In addition, the potential synergistic effects between chemicals further complicate the issue. In silico methods have become essential for tackling the problem from different perspectives, reducing the need for traditional in vivo testing, and saving valuable resources in terms of time and money. Artificial intelligence (AI) and machine learning (ML) are among today's advanced approaches for evaluating chemical hazards. They can serve, for instance, as a first-tier component of Integrated Approaches to Testing and Assessment (IATA). This study employed ML and AI to assess interactions between chemicals and specific biological targets within the AOP networks for cardiotoxicity, starting with molecular initiating events (MIEs) and progressing through key events (KEs). We explored methods to encode chemical information in a suitable way for ML and AI. We started with commonly used approaches in Quantitative Structure-Activity Relationship (QSAR) methods, such as molecular descriptors and different types of fingerprint. We then increased the complexity of encoders, incorporating graph-based methods, auto-encoders, and character embeddings employed in neural language processing. We also developed a multimodal neural network architecture, capable of considering the complementary nature of different chemical representations simultaneously. The potential of this approach, compared to more conventional architectures designed to handle a single encoder, becomes apparent when the amount of data increases.
心血管疾病是全球主要的死亡原因。来自不同类别化学品（如环境污染物、农药和药物）的潜在心脏毒性作用会对健康产生重大影响。同一化学物质可通过各种不良结局途径（AOPs）以不同方式诱发心脏毒性。此外，化学品之间的潜在协同效应使问题更加复杂。计算机模拟方法已成为从不同角度解决该问题、减少传统体内试验需求并在时间和金钱方面节省宝贵资源的关键。人工智能（AI）和机器学习（ML）是当今评估化学危害的先进方法。例如，它们可作为综合测试与评估方法（IATA）的一级组成部分。本研究采用ML和AI评估AOP网络中化学物质与心脏毒性特定生物靶点之间的相互作用，从分子起始事件（MIEs）开始，逐步推进到关键事件（KEs）。我们探索了以适合ML和AI的方式编码化学信息的方法。我们从定量构效关系（QSAR）方法中常用的方法入手，如分子描述符和不同类型的指纹。然后，我们增加了编码器的复杂性，纳入了基于图的方法、自动编码器以及神经语言处理中使用的字符嵌入。我们还开发了一种多模态神经网络架构，能够同时考虑不同化学表示的互补性。当数据量增加时，与旨在处理单个编码器的更传统架构相比，这种方法的潜力就会显现出来。
