AI驱动的多靶点药物分子设计评估框架：以脑疾病为案例研究
Evaluation Framework for AI-driven Molecular Design of Multi-target Drugs: Brain Diseases as a Case Study
AI总结：
本工作提出一个针对多靶点药物分子设计的评估框架，以脑疾病为案例，利用大语言模型选靶点、训练QSAR模型并评估四种深度生成模型和进化算法的性能，发现两者均具竞争力。
摘要：
人工智能（AI）技术的广泛应用显著影响了新治疗药物的开发。这些计算方法可用于设计和预测生成分子的性质。多靶点药物发现（MTDD）是一种新兴的药物发现范式，用于发现针对复杂疾病的药物，这些疾病对传统的靶点特异性治疗方法反应不佳，例如中枢神经系统、免疫系统和心血管疾病。然而，目前尚未建立一套用于评估AI工具设计多靶点化合物有效性的基准测试套件。标准化基准允许比较现有技术并促进快速研究进展。因此，本工作提出了一个针对MTDD场景中分子生成技术的评估框架，以脑疾病作为案例研究。我们的方法包括使用大型语言模型选择合适的分子靶点，收集和预处理生物测定数据集，训练定量构效关系模型以预测靶点调节，并评估其他必要的药物相似性属性以实施基准测试。此外，本工作将评估四种深度生成模型和进化算法在我们基准测试套件上的性能。在我们的发现中，进化算法和生成模型都能在提出的基准测试中取得有竞争力的结果。
英文摘要：
The widespread application of Artificial Intelligence (AI) techniques has significantly influenced the development of new therapeutic agents. These computational methods can be used to design and predict the properties of generated molecules. Multi-target Drug Discovery (MTDD) is an emerging paradigm for discovering drugs against complex disorders that do not respond well to more traditional target-specific treatments, such as central nervous system, immune system, and cardiovascular diseases. Still, there is yet to be an established benchmark suite for assessing the effectiveness of AI tools for designing multi-target compounds. Standardized benchmarks allow for comparing existing techniques and promote rapid research progress. Hence, this work proposes an evaluation framework for molecule generation techniques in MTDD scenarios, considering brain diseases as a case study. Our methodology involves using large language models to select the appropriate molecular targets, gathering and preprocessing the bioassay datasets, training quantitative structure-activity relationship models to predict target modulation, and assessing other essential drug-likeness properties for implementing the benchmarks. Additionally, this work will assess the performance of four deep generative models and evolutionary algorithms over our benchmark suite. In our findings, both evolutionary algorithms and generative models can achieve competitive results across the proposed benchmarks.
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。
