制药进展:将人工智能融入 QSAR、组合化学和绿色化学实践中

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摘要

在医疗和兽医治疗中使用药品不仅改善了人类和动物的健康,还促进了粮食生产和经济福利。然而,药品通过各种途径(如生产、人类排泄和不合标准的处置)释放到环境中,会对生态系统和与这些系统相关的各种生物实体产生有害影响。在生产设施的下游已经检测到了高浓度的药物残留,而未经处理的兽药残留最终会进入水体。利用人工智能(AI)和机器学习(ML)的方法已被用于建立化学结构与生物活性之间的联系,即化合物的定量结构-活性关系(QSAR)。QSAR 模型在缺乏实验数据的情况下,利用化学结构预测有害活性,从而帮助确定化学品的优先测试和编译顺序。组合化学通过实现高通量化合物合成,加快了目标分子的生成,以便在各个领域进行测试。绿色化学有助于创造、设计和实施化学产品和程序,目的是最大限度地减少或消除有害物质的产生和后续利用。此外,制药传感器技术(PST)是现代医学的重要工具,能够精确检测和监测各种生化和生理标记和参数。人工智能、ML、QSAR 建模以及组合化学和绿色化学方法的实施之间的协同作用对于推动创新产品和制药传感器技术的发展至关重要。这种跨学科方法对于在制药过程中创造降低毒性的解决方案至关重要,从而确保提高公共安全,促进环境资源的可持续发展。通过整合这些先进的方法,制药业可以实现更高的检测精度,提高环保产品的生产效率,最终实现更安全的制药和更健康的地球。
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Pharmaceutical advances: Integrating artificial intelligence in QSAR, combinatorial and green chemistry practices
The utilization of pharmaceuticals in medical and veterinary treatment has not only improved human and animal health but has also boosted food-production and economic welfare. However, the release of pharmaceuticals in the environment through various pathways, such as manufacturing, human excretion, and substandard disposal, can have detrimental effects on ecosystems and various biological entities associated with these systems. High levels of pharmaceutical residues have been detected further downstream of manufacturing facilities, and untreated veterinary medication leftovers can end up in waterbodies. Methods utilizing artificial intelligence (AI) and machine learning (ML) have been employed to establish connections between chemical structure and biological activity, referred to as quantitative structure–activity relationships (QSARs) for the compounds. QSAR models use chemical structures to predict hazardous activity when experimental data is lacking, thereby helping prioritize chemicals for testing and compilation. Combinatorial chemistry, by enabling high-throughput compound synthesis, accelerates the generation of targeted molecules for testing across various fields. Green chemistry helps in creating, designing, and implementing chemical products and procedures with the aim of minimizing or eradicating the generation and subsequent utilization of harmful substances. In addition, pharmaceutical sensor technologies (PST) are critical tools in modern medicine, enabling precise detection and monitoring of various biochemical and physiological markers and parameters. The synergy between AI, ML, QSAR modeling, and the implementation of combinatorial and green chemistry methodologies is pivotal in driving the development of innovative products and PST in pharmaceutics. This interdisciplinary approach is crucial for creating solutions with reduced toxicity in pharmaceutical processes, thereby ensuring enhanced public safety and promoting the sustainability of environmental resources. By integrating these advanced methodologies, the pharmaceutical industry can achieve greater detection accuracy, efficiency in production of eco-friendly products, ultimately leading to safer pharmaceutics and a healthier planet.
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