HelixADMET：一个强大的、端点可扩展的ADMET系统，包含自我监督的知识转移
HelixADMET: a robust and endpoint extensible ADMET system incorporating self-supervised knowledge transfer
摘要：
准确的ADMET（“吸收、分布、代谢、排泄和毒性”的缩写）预测可以在药物发现的早期阶段有效地筛选出不期望的候选药物。近年来，已经开发了采用先进的机器学习模型的多个综合ADMET系统，提供估计多个端点的服务。然而，这些ADMET系统通常遭受外推能力弱。首先，由于缺乏每个端点的标记数据，典型的机器学习模型对于具有未观察到的支架的分子表现脆弱。其次，大多数系统只提供固定的内置端点，无法定制以满足各种研究要求。为此，我们开发了一个强大的和端点可扩展的ADMET系统，HelixADMET（H-ADMET）。H-ADMET融合了自我监督学习的概念，以生成一个强大的预训练模型。然后使用多任务和多阶段框架对模型进行微调，以在ADMET端点、辅助任务和自我监督任务之间传输知识。我们的研究结果表明，与现有的ADMET系统相比，H-ADMET在可比终点上实现了4%的总体改善。此外，H-ADMET提供的预训练模型可以进行微调，以生成新的和定制的ADMET终点，满足药物研发的各种需求。
英文摘要：
Accurate ADMET (an abbreviation for "absorption, distribution, metabolism, excretion, and toxicity") predictions can efficiently screen out undesirable drug candidates in the early stage of drug discovery. In recent years, multiple comprehensive ADMET systems that adopt advanced machine learning models have been developed, providing services to estimate multiple endpoints. However, those ADMET systems usually suffer from weak extrapolation ability. First, due to the lack of labelled data for each endpoint, typical machine learning models perform frail for the molecules with unobserved scaffolds. Second, most systems only provide fixed built-in endpoints and cannot be customised to satisfy various research requirements. To this end, we develop a robust and endpoint extensible ADMET system, HelixADMET (H-ADMET). H-ADMET incorporates the concept of self-supervised learning to produce a robust pre-trained model. The model is then fine-tuned with a multi-task and multi-stage framework to transfer knowledge between ADMET endpoints, auxiliary tasks, and self-supervised tasks. Our results demonstrate that H-ADMET achieves an overall improvement of 4%, compared with existing ADMET systems on comparable endpoints. Additionally, the pre-trained model provided by H-ADMET can be fine-tuned to generate new and customised ADMET endpoints, meeting various demands of drug research and development requirements.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
