Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University , Jackson, MS, USA.
Expert Opin Drug Discov. 2020 Dec;15(12):1473-1487. doi: 10.1080/17460441.2020.1798926. Epub 2020 Jul 31.
We are in an era of bioinformatics and cheminformatics where we can predict data in the fields of medicine, the environment, engineering and public health. Approaches with open access tools have revolutionized disease management due to early prediction of the absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiles of the chemically designed and eco-friendly next-generation drugs.
This review meticulously encompasses the fundamental functions of open access prediction tools (webservers and standalone software) and advocates their use in drug discovery research for the safety and reliability of any candidate-drug. This review also aims to help support new researchers in the field of drug design.
The choice of tools is critically important for drug discovery and the accuracy of ADMET prediction. The accuracy largely depends on the types of dataset, the algorithm used, the quality of the model, the available endpoints for prediction, and user requirement. The key is to use multiple tools for predictions and comparing the results, followed by the identification of the most probable prediction.
我们正处于生物信息学和化学信息学时代，可以在医学、环境、工程和公共卫生等领域预测数据。由于能够早期预测化学设计和环保型下一代药物的吸收、分布、代谢、排泄和毒性 (ADMET) 特征，具有开放获取工具的方法彻底改变了疾病管理。
本文详细介绍了开放获取预测工具（网络服务器和独立软件）的基本功能，并主张将其用于药物发现研究，以确保候选药物的安全性和可靠性。本文还旨在为药物设计领域的新研究人员提供帮助。
工具的选择对于药物发现和 ADMET 预测的准确性至关重要。准确性在很大程度上取决于数据集的类型、使用的算法、模型的质量、可用于预测的终点以及用户需求。关键是使用多种工具进行预测并比较结果，然后确定最有可能的预测结果。
