Department of Biochemistry, College of Science, Evangel University, Akaeze, Ebonyi State, Nigeria.
Department of Computer Sciences, Our Savior Institute of Science, Agriculture, and Technology (OSISATECH Polytechnic), Enugu, Nigeria.
Drug Dev Res. 2024 Apr;85(2):e22159. doi: 10.1002/ddr.22159.
The purpose of this study was to discuss how artificial intelligence (AI) methods have affected the field of drug development. It looks at how AI models and data resources are reshaping the drug development process by offering more affordable and expedient options to conventional approaches. The paper opens with an overview of well-known information sources for drug development. The discussion then moves on to molecular representation techniques that make it possible to convert data into representations that computers can understand. The paper also gives a general overview of the algorithms used in the creation of drug discovery models based on AI. In particular, the paper looks at how AI algorithms might be used to forecast drug toxicity, drug bioactivity, and drug physicochemical properties. De novo drug design, binding affinity prediction, and other AI-based models for drug-target interaction were covered in deeper detail. Modern applications of AI in nanomedicine design and pharmacological synergism/antagonism prediction were also covered. The potential advantages of AI in drug development are highlighted as the evaluation comes to a close. It underlines how AI may greatly speed up and improve the efficiency of drug discovery, resulting in the creation of new and better medicines. To fully realize the promise of AI in drug discovery, the review acknowledges the difficulties that come with its uses in this field and advocates for more study and development.
本研究旨在探讨人工智能 (AI) 方法如何影响药物开发领域。本文着眼于 AI 模型和数据资源如何通过提供更经济实惠和便捷的选择来改变药物开发过程，从而对传统方法进行重塑。本文开篇概述了药物开发的知名信息来源。接着，讨论了分子表示技术，这些技术使将数据转换为计算机可以理解的表示形式成为可能。本文还概述了基于 AI 的药物发现模型创建中使用的算法。具体而言，本文探讨了 AI 算法如何用于预测药物毒性、药物生物活性和药物物理化学性质。本文更深入地探讨了基于 AI 的新药设计、结合亲和力预测和其他药物-靶标相互作用模型。本文还涵盖了 AI 在纳米医学设计和药理学协同/拮抗作用预测中的现代应用。在评估接近尾声时，强调了 AI 在药物开发中的潜在优势。它强调了 AI 如何极大地加快和提高药物发现的效率，从而创造出新药和更好的药物。为了充分实现 AI 在药物发现中的潜力，本文承认在该领域使用 AI 所带来的困难，并提倡进行更多的研究和开发。
