Han Ri, Yoon Hongryul, Kim Gahee, Lee Hyundo, Lee Yoonji
College of Pharmacy, Chung-Ang University, Seoul 06974, Republic of Korea.
Pharmaceuticals (Basel). 2023 Sep 6;16(9):1259. doi: 10.3390/ph16091259.
Artificial intelligence (AI) has permeated various sectors, including the pharmaceutical industry and research, where it has been utilized to efficiently identify new chemical entities with desirable properties. The application of AI algorithms to drug discovery presents both remarkable opportunities and challenges. This review article focuses on the transformative role of AI in medicinal chemistry. We delve into the applications of machine learning and deep learning techniques in drug screening and design, discussing their potential to expedite the early drug discovery process. In particular, we provide a comprehensive overview of the use of AI algorithms in predicting protein structures, drug-target interactions, and molecular properties such as drug toxicity. While AI has accelerated the drug discovery process, data quality issues and technological constraints remain challenges. Nonetheless, new relationships and methods have been unveiled, demonstrating AI's expanding potential in predicting and understanding drug interactions and properties. For its full potential to be realized, interdisciplinary collaboration is essential. This review underscores AI's growing influence on the future trajectory of medicinal chemistry and stresses the importance of ongoing synergies between computational and domain experts.
人工智能（AI）已经渗透到各个领域，包括制药行业和研究领域，在这些领域中，它被用于高效地识别具有理想特性的新化学实体。将人工智能算法应用于药物发现既带来了显著的机遇，也带来了挑战。这篇综述文章聚焦于人工智能在药物化学中的变革性作用。我们深入探讨机器学习和深度学习技术在药物筛选和设计中的应用，讨论它们加速早期药物发现过程的潜力。特别是，我们全面概述了人工智能算法在预测蛋白质结构、药物-靶点相互作用以及药物毒性等分子特性方面的应用。虽然人工智能加速了药物发现过程，但数据质量问题和技术限制仍然是挑战。尽管如此，新的关系和方法已经被揭示出来，展示了人工智能在预测和理解药物相互作用及特性方面不断扩大的潜力。为了充分发挥其潜力，跨学科合作至关重要。这篇综述强调了人工智能对药物化学未来发展轨迹日益增长的影响，并强调了计算专家和领域专家之间持续协同合作的重要性。
