Chair of Molecular Technology, Institute of Chemistry, University of Tartu, Tartu, Estonia.
Expert Opin Drug Discov. 2024 Feb;19(2):131-137. doi: 10.1080/17460441.2023.2277342. Epub 2024 Feb 1.
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.
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.
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.
现代药物发现结合了各种工具和数据，标志着数据驱动的药物设计（DD）时代的开始。因此，用于人工智能（AI）/机器学习（ML）和推动 DD 的化学和物理数据的分布变得非常重要，需要理解并有效利用。
作者全面探索了推动药物发现数据密集型时代的统计分布，包括基于贝叶斯定律的 AI/ML 驱动的 DD。
随着数据驱动发现的相关性不断提高，我们预计将利用贝叶斯定律等原则对数据集进行细致审查，以提高数据完整性，并指导高效的资源分配和实验规划。在制药和医疗行业的数据驱动时代，解决偏差缓解、算法有效性、数据治理、影响和欺诈预防等关键方面至关重要。在 DD 中利用贝叶斯定律和其他分布和统计检验提供了一种强大的策略，可以检测数据异常、填补数据空白并提高数据集质量。贝叶斯定律是一种快速的数据完整性和数据集质量方法，是 AI/ML 和其他建模方法的基础，在设计过程中非常有用。
