In-Silico Approaches for Drug Designing Technology: Bridging Discovery and Development.

Aminul Islam, Diptimayee Jena, Nur Shaid Mondal, Aniya Teli, Sandip Mondal, Manish Kumar Gautam
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Abstract

Traditional drug discovery processes have disadvantages such as efficiency, cost, and high attrition rates. In silico methods, involving computational simulations and modelling, offer powerful solutions to bridge the gap between discovery and development. This review explores various in silico approaches, including ligand-based and structure-based drug design, virtual screening, molecular docking, and ADMET prediction. We explore their utilization throughout different phases of phar-maceutical development, spanning from target identification and lead refinement to forecasting tox-icity and pharmacokinetics. In-silico methods enable rapid lead identification and optimization, re-ducing reliance on expensive wet lab experiments. They contribute to improved drug quality by pre-dicting ADMET properties and off-target effects, ultimately accelerating development timelines and lowering costs. In silico approaches are revolutionizing drug design by providing predictive and cost-effective solutions. Incorporating them into the design process streamlines lead refinement and en-hances the likelihood of success for potential drugs, ultimately expediting the translation of innova-tive treatments to patients.

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药物设计技术的计算机方法:连接发现与开发。
传统的药物发现过程存在效率低、成本高、人员流失率高等缺点。计算机方法,包括计算模拟和建模,为弥合发现和开发之间的差距提供了强大的解决方案。这篇综述探讨了各种计算机方法,包括基于配体和基于结构的药物设计、虚拟筛选、分子对接和ADMET预测。我们探索它们在药物开发的不同阶段的应用,从目标识别和导联改进到预测毒性和药代动力学。硅片方法能够快速识别和优化铅,减少对昂贵的湿实验室实验的依赖。通过预测ADMET的特性和脱靶效应,它们有助于提高药物质量,最终加快开发时间并降低成本。通过提供预测性和成本效益的解决方案,计算机方法正在彻底改变药物设计。将它们纳入设计过程可以简化药物的改进,提高潜在药物成功的可能性,最终加快创新治疗对患者的转化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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