Hashemi Samaneh, Vosough Parisa, Taghizadeh Saeed, Savardashtaki Amir
Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran.
Department of Medical Biotechnology, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, Iran.
Heliyon. 2024 Nov 8;10(22):e40265. doi: 10.1016/j.heliyon.2024.e40265. eCollection 2024 Nov 30.
Due to the spread of antibiotic resistance, global attention is focused on its inhibition and the expansion of effective medicinal compounds. The novel functional properties of peptides have opened up new horizons in personalized medicine. With artificial intelligence methods combined with therapeutic peptide products, pharmaceuticals and biotechnology advance drug development rapidly and reduce costs. Short-chain peptides inhibit a wide range of pathogens and have great potential for targeting diseases. To address the challenges of synthesis and sustainability, artificial intelligence methods, namely machine learning, must be integrated into their production. Learning methods can use complicated computations to select the active and toxic compounds of the drug and its metabolic activity. Through this comprehensive review, we investigated the artificial intelligence method as a potential tool for finding peptide-based drugs and providing a more accurate analysis of peptides through the introduction of predictable databases for effective selection and development.
由于抗生素耐药性的传播，全球关注的焦点在于抑制耐药性以及扩展有效的药用化合物。肽的新型功能特性为个性化医疗开辟了新的视野。通过将人工智能方法与治疗性肽产品相结合，制药和生物技术领域迅速推进药物研发并降低成本。短链肽能抑制多种病原体，在疾病靶向治疗方面具有巨大潜力。为应对合成和可持续性方面的挑战，必须将人工智能方法，即机器学习，融入其生产过程。学习方法可以利用复杂的计算来筛选药物的活性和毒性化合物及其代谢活性。通过这篇全面综述，我们研究了人工智能方法作为寻找基于肽的药物的潜在工具，并通过引入可预测数据库以进行有效筛选和开发，从而对肽进行更准确的分析。
