Target-specific machine-learning scoring functions for reliable structure-based virtual screening

针对特定目标的机器学习评分功能，用于可靠的基于结构的虚拟筛选

• 批准号: EP/X012026/1
• 负责人: Pedro Ballester
• 金额: $ 79.01万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FX012026%2F1
• 关键词: Target; specific; machine; learning; scoring

Drug leads are typically small-sized chemical compounds that tightly bind to a disease-causing protein. As the right key fits into a lock, a drug lead molecule acts by binding to the right pocket of its therapeutic target protein to potently alter its function in a way that positively impacts the associated disease. Academic research excels at discovering promising therapeutic targets. However, discovering drug leads and optimising their potency for a target is an expensive, time-consuming and particularly challenging process, which has traditionally been carried out by pharmaceutical companies with vast resources. This constitutes a barrier to translating innovative biomedical research from academia into new drug candidates, as an optimised lead is required to attract funding for further preclinical and clinical studies via out-licensing or industry partnership. Tools are therefore needed to help academics to bridge this translation gap by reducing the experimental efforts required, or even making it possible, to achieve optimised drug leads for a given target.Docking is a computational technique providing relatively fast predictions of whether and how a molecule binds to an atomic-resolution structure of the target. Very recently, the exploitation of a novel technology generating billions of make-on-demand molecules by classical docking tools have directly achieved a range of diverse and potent drug leads for several targets. Therefore, no lengthy and costly optimisation was subsequently required, thereby strongly reducing the time and cost to provide these optimised drug leads. However, the modest predictive performance of these classical tools is extensively documented. This means that their application to many other targets is likely to be much worse than that in the few targets reported so far. It is now well-known that a way to boost docking performance in other targets is by enhancing it with Artificial Intelligence (AI). Unlike the classical tools, AI models can exploit fast-growing datasets to learn to discriminate between molecules with or without potent activity for the target. AI models are furthermore likely to make drug design even faster and less expensive than the classical tools on those targets where the latter work well.This methodology research project aims at improving target structure-based drug design via the innovative application of AI techniques. We will investigate optimal ways to build target-specific AI models. For the first time, these AI models will be generated in a way that not only predicts how strongly the molecule binds to the target, but also how reliable that prediction is. We will compare the predictive accuracy of these models to that of existing models for any target, whether classical or AI-based, using the most rigorous retrospective assessment practices. We will also investigate to which extent coupling these models with ultrafast, yet less accurate, models can directly provide optimised drug leads in a fraction of the time.The project focuses on those targets for which at least some binding molecules are available. Practically all currently investigated targets have never been analysed with structure-based target-specific AI models. Here we will develop and apply AI models tailored to two of such targets, which are in addition not related to those for which ultra-large library screening has been reported so far. The discovery of the first of these targets, TRPM8, has just been partly awarded the 2021 Nobel Prize in Physiology or Medicine. Drugs targeting TRPM8 should be able to mitigate cold hypersensitivity and pain. The second target, ATM, could be a way to provide new therapeutic options for those suffering from Huntington's disease and various types of cancers including brain tumours.We will provide all the data, codes and documentation to facilitate reproducibility and future research for further improvements on these and related targets.

药物先导物通常是与致病蛋白质紧密结合的小分子化合物。当正确的钥匙适合锁时，药物先导分子通过结合其治疗靶蛋白的右口袋来发挥作用，以积极影响相关疾病的方式有效地改变其功能。学术研究擅长发现有前途的治疗靶点。然而，发现药物先导化合物并优化其对靶点的效力是一个昂贵、耗时且特别具有挑战性的过程，传统上由拥有大量资源的制药公司进行。这构成了将学术界的创新生物医学研究转化为新药候选药物的障碍，因为需要优化的领先优势才能通过外部许可或行业合作关系为进一步的临床前和临床研究吸引资金。因此，需要一些工具来帮助学术界弥合这一翻译差距，减少所需的实验工作，甚至使之成为可能，以实现针对给定靶标的优化药物先导。对接是一种计算技术，可以相对快速地预测分子是否以及如何与靶标的原子分辨率结构结合。最近，通过经典对接工具产生数十亿个按需制造分子的新技术的开发已经直接实现了针对若干靶点的一系列多样且有效的药物先导物。因此，随后不需要冗长和昂贵的优化，从而大大减少了提供这些优化的药物先导物的时间和成本。然而，这些经典工具的适度预测性能被广泛记录。这意味着，它们对许多其他目标的适用情况可能比迄今报告的少数目标差得多。现在众所周知，提高其他目标对接性能的一种方法是通过人工智能（AI）来增强它。与经典工具不同，人工智能模型可以利用快速增长的数据集来学习区分具有或不具有靶点活性的分子。人工智能模型还可能使药物设计比经典工具更快，更便宜，后者工作得很好。本方法学研究项目旨在通过人工智能技术的创新应用来改进基于靶结构的药物设计。我们将研究构建目标特定AI模型的最佳方法。这是第一次，这些人工智能模型的生成方式不仅可以预测分子与靶标结合的强度，还可以预测的可靠性。我们将使用最严格的回顾性评估实践，将这些模型的预测准确性与任何目标的现有模型的预测准确性进行比较，无论是经典模型还是基于AI的模型。我们还将研究在何种程度上将这些模型与超快但不太准确的模型相结合，可以在一小部分时间内直接提供优化的药物线索。该项目侧重于那些至少有一些结合分子可用的靶点。实际上，目前研究的所有目标从未使用基于结构的目标特定AI模型进行过分析。在这里，我们将开发和应用针对其中两个目标量身定制的人工智能模型，这些目标与迄今为止报道的超大库筛选无关。其中第一个靶点TRPM8的发现刚刚被部分授予2021年诺贝尔生理学或医学奖。靶向TRPM8的药物应该能够减轻冷过敏和疼痛。第二个靶点是ATM，它可以为患有亨廷顿病和包括脑肿瘤在内的各种癌症的患者提供新的治疗选择。我们将提供所有的数据、代码和文件，以促进可重复性和未来的研究，进一步改进这些和相关的靶点。