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通过制药领域的前沿研究实现药物发现自动化:挑战与未来展望。

Automation of Drug Discovery through Cutting-edge Research in Pharmaceuticals: Challenges and Future Scope.

机构信息

Department of Pharmaceutics, SRM Modinagar College of Pharmacy, SRM Institute of Science and Technology, Delhi NCR Campus, Modinagar, Ghaziabad, India.

KIET School of Pharmacy, KIET Group of Institutions, Ghaziabad, India.

出版信息

Curr Comput Aided Drug Des. 2024;20(6):723-735. doi: 10.2174/0115734099260187230921073932.

Abstract

The rapidity and high-throughput nature of technologies make them advantageous for predicting the properties of a large array of substances. approaches can be used for compounds intended for synthesis at the beginning of drug development when there is either no or very little compound available. approaches can be used for impurities or degradation products. Quantifying drugs and related substances (RS) with pharmaceutical drug analysis (PDA) can also improve drug discovery (DD) by providing additional avenues to pursue. Potential future applications of PDA include combining it with other methods to make insilico predictions about drugs and RS. One possible outcome of this is a determination of the drug potential of nontoxic RS. ADME estimation, QSAR research, molecular docking, bioactivity prediction, and toxicity testing all involve impurity profiling. Before committing to DD, RS with minimal toxicity can be utilised in silico. The efficacy of molecular docking in getting a medication to market is still debated despite its refinement and improvement. Biomedical labs and pharmaceutical companies were hesitant to adopt molecular docking algorithms for drug screening despite their decades of development and improvement. Despite the widespread use of "force fields" to represent the energy exerted within and between molecules, it has been impossible to reliably predict or compute the binding affinities between proteins and potential binding medications.

摘要

技术的快速性和高通量性质使它们有利于预测大量物质的性质。在药物开发的早期,当合成的化合物没有或只有很少的化合物可用时,可以使用虚拟筛选方法。虚拟筛选方法也可以用于杂质或降解产物。使用药物分析(PDA)定量药物和相关物质(RS)也可以通过提供更多的途径来改善药物发现(DD)。PDA 的潜在未来应用包括将其与其他方法结合使用,以便对药物和 RS 进行计算预测。这样做的一个可能结果是确定非毒性 RS 的药物潜力。ADME 估计、QSAR 研究、分子对接、生物活性预测和毒性测试都涉及杂质分析。在致力于 DD 之前,可以在计算机上使用最小毒性的 RS。尽管分子对接进行了改进和完善,但它在使药物上市方面的功效仍存在争议。尽管分子对接算法已经开发和改进了几十年,但生物医学实验室和制药公司仍不愿将其用于药物筛选。尽管已经使用“力场”来表示分子内和分子间的能量,但仍不可能可靠地预测或计算蛋白质与潜在结合药物之间的结合亲和力。

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