Kumar Prashant, Mahor Alpana, Tomar Roopam
IIMT College of Pharmacy, Plot no. 19 & 20, Knowledge Park III, Greater Noida, Uttar Pradesh, 201310, India.
Chandigarh University, NH-05, Ludhiana Highway, Chandigarh State, Punjab, 140413, India.
Curr Drug Discov Technol. 2025 Feb 10. doi: 10.2174/0115701638364199250123062248.
Drug design and development are crucial areas of study for chemists and pharmaceutical companies. Nevertheless, the significant expenses, lengthy process, inaccurate delivery, and limited effectiveness present obstacles and barriers that affect the development and exploration of new drugs. Moreover, big and complex datasets from clinical trials, genomics, proteomics, and microarray data also disrupt the drug discovery approach. The integration of Artificial Intelligence (AI) into drug design is both timely and crucial due to several pressing challenges in the pharmaceutical industry, including the escalating costs of drug development, high failure rates in clinical trials, and the in-creasing complexity of disease biology. AI offers innovative solutions to address these challenges, promising to improve the efficiency, precision, and success rates of drug discovery and development. Artificial intelligence (AI) and machine learning (ML) technology are crucial tools in the field of drug discovery and development. More precisely, the field has been revolutionized by the utilization of deep learning (DL) techniques and artificial neural networks (ANNs). DL algorithms & ML have been employed in drug design using various approaches such as physiochemical activity, polyphar-macology, drug repositioning, quantitative structure-activity relationship, pharmacophore modeling, drug monitoring and release, toxicity prediction, ligand-based virtual screening, structure-based vir-tual screening, and peptide synthesis. The use of DL and AI in this field is supported by historical evidence. Furthermore, management strategies, curation, and unconventional data mining aided as-sistance in modern modeling algorithms. In summary, the progress made in artificial intelligence and deep learning algorithms offers a promising opportunity for the development and discovery of effec-tive drugs, ultimately leading to significant benefits for humanity. In this review, several tools and algorithmic programs have been discussed which are being used in drug design along with the de-scriptions of the patents that have been granted for the use of AI in this field, which constitutes the main focus of this review and differentiates it fromalready published materials.
药物设计与开发是化学家和制药公司至关重要的研究领域。然而，高昂的成本、漫长的过程、不准确的给药方式以及有限的疗效构成了影响新药研发与探索的障碍。此外，来自临床试验、基因组学、蛋白质组学和微阵列数据的大量复杂数据集也扰乱了药物发现方法。由于制药行业面临的诸多紧迫挑战，包括药物开发成本不断上升、临床试验失败率高以及疾病生物学日益复杂，将人工智能（AI）融入药物设计既及时又至关重要。人工智能为应对这些挑战提供了创新解决方案，有望提高药物发现与开发的效率、精度和成功率。人工智能（AI）和机器学习（ML）技术是药物发现与开发领域的关键工具。更确切地说，深度学习（DL）技术和人工神经网络（ANNs）的应用彻底改变了该领域。DL算法和ML已通过多种方法应用于药物设计，如物理化学活性、多药理学、药物重新定位、定量构效关系、药效团建模、药物监测与释放、毒性预测、基于配体的虚拟筛选、基于结构的虚拟筛选以及肽合成。该领域对DL和AI的使用有历史证据支持。此外，管理策略、数据整理以及非常规数据挖掘辅助了现代建模算法。总之，人工智能和深度学习算法取得的进展为有效药物的开发与发现提供了充满希望的机会，最终为人类带来巨大益处。在本综述中，讨论了几种用于药物设计的工具和算法程序，以及该领域因使用AI而获批的专利描述，这构成了本综述的主要重点，并使其有别于已发表的材料。
