Implementation of an Automated System Using Machine Learning Models to Accelerate the Process of In Silico Identification of Small Molecules As Drug Candidates.
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引用次数: 0
Abstract
Drugs are commonly utilized to diagnose, cure, or prevent the occurrence of diseases, as well as to restore, alter, or change organic functions. Drug discovery is a time-consuming, costly, difficult, and inefficient process that yields very few medicinal breakthroughs. Drug research and design involves the capturing of structural information for biological targets and small molecules as well as various in silico methods, such as molecular docking and molecular dynamic simulation. This article proposes the idea of expediting computational drug development through a collaboration of scientists and universities, similar to the Human Genome Project using machine learning (ML) strategies. We envision an automated system where readily available or novel small molecules (chemical or plant-derived), as well as their biological targets, are uploaded to an online database, which is constantly updated. For this system to function, machine learning strategies have to be implemented, and high-quality datasets and high quality assurance of the ML models will be required. ML can be applied to all computational drug discovery fields, including hit discovery, target validation, lead optimization, drug repurposing, and data mining of small compounds and biomolecule structures. Researchers from various disciplines, such as bioengineers, bioinformaticians, geneticists, chemists, computer and software engineers, and pharmacists, are expected to collaborate to establish a solid workflow and certain parameters as well as constraints for a successful outcome. This automated system may help speed up the drug discovery process while also lowering the number of unsuccessful drug candidates. Additionally, this system will decrease the workload, especially in computational studies, and expedite the process of drug design. As a result, a drug may be manufactured in a relatively short time.
药物通常用于诊断、治疗或预防疾病的发生,以及恢复、改变或改变机体功能。药物发现是一个耗时、耗资、困难和低效的过程,很少能在医学上取得突破。药物研究和设计涉及捕捉生物靶标和小分子的结构信息,以及各种硅学方法,如分子对接和分子动态模拟。本文提出了通过科学家和大学合作加快计算药物开发的想法,类似于使用机器学习(ML)策略的人类基因组计划。我们设想建立一个自动化系统,将现成的或新颖的小分子(化学或植物来源)及其生物靶标上传到在线数据库,并不断更新。要使这一系统发挥作用,必须实施机器学习策略,并且需要高质量的数据集和高质量的 ML 模型保证。ML 可应用于所有计算药物发现领域,包括命中发现、靶点验证、先导优化、药物再利用以及小化合物和生物分子结构的数据挖掘。来自不同学科的研究人员,如生物工程师、生物信息学家、遗传学家、化学家、计算机和软件工程师以及药剂师等,应通力合作,建立稳固的工作流程和某些参数及限制条件,以取得成功的结果。这一自动化系统可能有助于加快药物发现过程,同时减少不成功候选药物的数量。此外,该系统还能减少工作量,尤其是计算研究方面的工作量,并加快药物设计过程。因此,可以在相对较短的时间内制造出药物。
期刊介绍:
Aims & Scope
Current Medicinal Chemistry covers all the latest and outstanding developments in medicinal chemistry and rational drug design. Each issue contains a series of timely in-depth reviews and guest edited thematic issues written by leaders in the field covering a range of the current topics in medicinal chemistry. The journal also publishes reviews on recent patents. Current Medicinal Chemistry is an essential journal for every medicinal chemist who wishes to be kept informed and up-to-date with the latest and most important developments.