计算机辅助药物发现的人工智能。

IF 1.7 Q3 PHARMACOLOGY & PHARMACY Drug Research Pub Date : 2023-09-01 DOI:10.1055/a-2076-3359
Aditya Kate, Ekkita Seth, Ananya Singh, Chandrashekhar Mahadeo Chakole, Meenakshi Kanwar Chauhan, Ravi Kant Singh, Shrirang Maddalwar, Mohit Mishra
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引用次数: 1

摘要

人工智能(AI)在多个科学领域的持续实施以及计算机软件和硬件的快速进步,以及其他参数,迅速推动了这一发展。该技术可以有效地解决传统药物开发中的许多挑战和限制。传统上,通过筛选大型化学文库来发现一种有前景的药物。近年来,更合理的基于结构的药物设计方法已经避免了第一个筛选阶段,但仍然需要化学家设计、合成和测试广泛的化合物来生产可能的新药物。将一种有前途的化学物质转化为候选药物的过程既昂贵又耗时。此外,一种新的候选药物即使在实验室研究中表现出希望,也可能在临床试验中失败。事实上,只有不到10%的候选药物通过I期试验真正进入市场。因此,人工智能系统无与伦比的数据处理能力可能会以四种不同的方式加速和加强药物开发过程:打开与新型生物系统的联系,优越或独特的化学,更高的成功率,以及更快、更便宜的创新试验。由于这些技术可能用于处理各种发现场景和生物目标,因此理解和区分用例是必不可少的。因此,我们强调了如何将人工智能应用于制药科学的各个领域,包括药物研究和开发的深入机会。
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Artificial Intelligence for Computer-Aided Drug Discovery.

The continuous implementation of Artificial Intelligence (AI) in multiple scientific domains and the rapid advancement in computer software and hardware, along with other parameters, have rapidly fuelled this development. The technology can contribute effectively in solving many challenges and constraints in the traditional development of the drug. Traditionally, large-scale chemical libraries are screened to find one promising medicine. In recent years, more reasonable structure-based drug design approaches have avoided the first screening phases while still requiring chemists to design, synthesize, and test a wide range of compounds to produce possible novel medications. The process of turning a promising chemical into a medicinal candidate can be expensive and time-consuming. Additionally, a new medication candidate may still fail in clinical trials even after demonstrating promise in laboratory research. In fact, less than 10% of medication candidates that undergo Phase I trials really reach the market. As a consequence, the unmatched data processing power of AI systems may expedite and enhance the drug development process in four different ways: by opening up links to novel biological systems, superior or distinctive chemistry, greater success rates, and faster and less expensive innovation trials. Since these technologies may be used to address a variety of discovery scenarios and biological targets, it is essential to comprehend and distinguish between use cases. As a result, we have emphasized how AI may be used in a variety of areas of the pharmaceutical sciences, including in-depth opportunities for drug research and development.

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来源期刊
Drug Research
Drug Research PHARMACOLOGY & PHARMACY-
CiteScore
3.50
自引率
0.00%
发文量
67
期刊介绍: Drug Research (formerly Arzneimittelforschung) is an international peer-reviewed journal with expedited processing times presenting the very latest research results related to novel and established drug molecules and the evaluation of new drug development. A key focus of the publication is translational medicine and the application of biological discoveries in the development of drugs for use in the clinical environment. Articles and experimental data from across the field of drug research address not only the issue of drug discovery, but also the mathematical and statistical methods for evaluating results from industrial investigations and clinical trials. Publishing twelve times a year, Drug Research includes original research articles as well as reviews, commentaries and short communications in the following areas: analytics applied to clinical trials chemistry and biochemistry clinical and experimental pharmacology drug interactions efficacy testing pharmacodynamics pharmacokinetics teratology toxicology.
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