Benford定律和更好的药物设计分布。

IF 6 2区 医学 Q1 PHARMACOLOGY & PHARMACY Expert Opinion on Drug Discovery Pub Date : 2024-02-01 DOI:10.1080/17460441.2023.2277342
Alfonso T García-Sosa
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引用次数: 0

摘要

引言:现代药物发现融合了各种工具和数据,预示着数据驱动药物设计(DD)时代的开始。因此,用于人工智能(AI)/机器学习(ML)和驱动DD的化学和物理数据的分布对于有效理解和使用变得非常重要。涵盖的领域:作者对推动药物发现数据密集型时代的统计分布进行了全面探索,包括基于AI/ML的DD中的Benford定律。专家意见:随着数据驱动发现的相关性升级,我们预计将利用Benford定律等原理对数据集进行细致的审查,以增强数据的完整性,并指导有效的资源分配和实验规划。在这个制药和医疗行业的数据驱动时代,解决偏见缓解、算法有效性、数据管理、效果和欺诈预防等关键方面至关重要。利用Benford定律和DD中的其他分布和统计测试提供了一种有效的策略来检测数据异常、填补数据空白和提高数据集质量。Benford定律是一种快速的数据完整性和数据集质量方法,是AI/ML和其他建模方法的支柱,在设计过程中非常有用。
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Benford's Law and distributions for better drug design.

Introduction: Modern drug discovery incorporates various tools and data, heralding the beginning of the data-driven drug design (DD) era. The distributions of chemical and physical data used for Artificial Intelligence (AI)/Machine Learning (ML) and to drive DD have thus become highly important to be understood and used effectively.

Areas covered: The authors perform a comprehensive exploration of the statistical distributions driving the data-intensive era of drug discovery, including Benford's Law in AI/ML-based DD.

Expert opinion: As the relevance of data-driven discovery escalates, we anticipate meticulous scrutiny of datasets utilizing principles like Benford's Law to enhance data integrity and guide efficient resource allocation and experimental planning. In this data-driven era of the pharmaceutical and medical industries, addressing critical aspects such as bias mitigation, algorithm effectiveness, data stewardship, effects, and fraud prevention are essential. Harnessing Benford's Law and other distributions and statistical tests in DD provides a potent strategy to detect data anomalies, fill data gaps, and enhance dataset quality. Benford's Law is a fast method for data integrity and quality of datasets, the backbone of AI/ML and other modeling approaches, proving very useful in the design process.

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来源期刊
CiteScore
10.20
自引率
1.60%
发文量
78
审稿时长
6-12 weeks
期刊介绍: Expert Opinion on Drug Discovery (ISSN 1746-0441 [print], 1746-045X [electronic]) is a MEDLINE-indexed, peer-reviewed, international journal publishing review articles on novel technologies involved in the drug discovery process, leading to new leads and reduced attrition rates. Each article is structured to incorporate the author’s own expert opinion on the scope for future development. The Editors welcome: Reviews covering chemoinformatics; bioinformatics; assay development; novel screening technologies; in vitro/in vivo models; structure-based drug design; systems biology Drug Case Histories examining the steps involved in the preclinical and clinical development of a particular drug The audience consists of scientists and managers in the healthcare and pharmaceutical industry, academic pharmaceutical scientists and other closely related professionals looking to enhance the success of their drug candidates through optimisation at the preclinical level.
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