QUANTUM-TOX - Revolutionizing Computational Toxicology with Electronic Structure Descriptors and Artificial Intelligence

QUANTUM-TOX - 利用电子结构描述符和人工智能彻底改变计算毒理学

• 批准号: 10106704
• 金额: $ 67.39万
• 依托单位: THE UNIVERSITY OF MANCHESTER
• 依托单位国家: 英国
• 项目类别: EU-Funded
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=10106704
• 关键词: QUANTUM; TOX; Revolutionizing; Computational; Toxicology

Toxicology is at a crossroads. With ever more drugs going to market and more chemicals having an environmental impact, the need for fast, cheap and accurate technologies to assess toxic effects is pressing. Computational toxicology provides an array of tools and methods for toxicity prediction only using computer approaches. Conceptually, computational toxicology has significant advantages since testing is fast and cheaper than in vitro. However, currently computational toxicology has severe limitations. Predictions typically use Quantitative Structure-Activity Relationship (QSAR) models that rely on large sets of molecular descriptors. This causes severe problems since the methodologies cannot assess chemicals different than the ones used to develop the QSAR models, and when that is possible, the very large number of descriptors limits understandability. Therefore, new methodologies are needed to address those shortcomings. This project will develop a new type of descriptor, totally based on quantum mechanics that can cover the whole chemical space and relies on a small number of parameters that are easily interpretable. Starting with meaningful chemical perturbations, that extract the behaviour of the chemicals in assumed mechanisms of toxic action, the approach will develop specific Electronic SIGNatures (ESigns). ESigns are mathematical invariants that map the results of the quantum chemical calculations. Using Artificial Intelligence, the ESigns will relate to toxicity. The new approach introduces a momentous change in computational toxicology. It can cover the whole chemical space, since we are abandoning predictions based on molecular structures, uses fewer parameters, and can be related to the new trends in toxicology regarding use of pathways information. In fact, it is a powerful tool to allow accurate toxicology predictions solely on the basis of biochemical and chemical insight.

毒理学正处于十字路口。随着越来越多的药物进入市场，越来越多的化学品对环境产生影响，迫切需要快速，廉价和准确的技术来评估毒性作用。计算毒理学提供了一系列的工具和方法，毒性预测只使用计算机的方法。从概念上讲，计算毒理学具有显着的优势，因为测试速度快，比体外便宜。然而，目前计算毒理学有严重的局限性。预测通常使用依赖于大量分子描述符的定量结构-活性关系（QSAR）模型。这造成了严重的问题，因为这些方法无法评估与用于开发定量构效关系模型的化学品不同的化学品，而在可能的情况下，大量的描述符限制了理解。因此，需要新的方法来解决这些缺点。该项目将开发一种新型的描述符，完全基于量子力学，可以覆盖整个化学空间，并依赖于少量易于解释的参数。从有意义的化学扰动开始，提取化学品在假定的毒性作用机制中的行为，该方法将开发特定的电子签名（ESigns）。ESigns是映射量子化学计算结果的数学不变量。使用人工智能，ESigns将与毒性有关。这种新方法在计算毒理学领域带来了重大变化。它可以覆盖整个化学空间，因为我们放弃了基于分子结构的预测，使用更少的参数，并且可以与毒理学中关于使用途径信息的新趋势相关。事实上，它是一个强大的工具，允许仅基于生物化学和化学见解进行准确的毒理学预测。