Trachtenberg Alexander, Akabayov Barak
Department of Chemistry and Data Science Research Center, Ben-Gurion University of the Negev, Beer-Sheva 8410501, Israel.
Pharmaceutics. 2025 May 5;17(5):612. doi: 10.3390/pharmaceutics17050612.
In today's information-driven era, machine learning is revolutionizing medicinal chemistry, offering a paradigm shift from traditional, intuition-based, and often bias-prone methods to the prediction of chemical properties without prior knowledge of the basic principles governing drug function. This perspective highlights the growing importance of informatics in shaping the field of medicinal chemistry, particularly through the concept of the "". The informacophore refers to the minimal chemical structure, combined with computed molecular descriptors, fingerprints, and machine-learned representations of its structure, that are essential for a molecule to exhibit biological activity. Similar to a skeleton key unlocking multiple locks, the informacophore points to the molecular features that trigger biological responses. By identifying and optimizing informacophores through in-depth analysis of ultra-large datasets of potential lead compounds and automating standard parts in the development process, there will be a significant reduction in biased intuitive decisions, which may lead to systemic errors and a parallel acceleration of drug discovery processes.
在当今信息驱动的时代，机器学习正在彻底改变药物化学，实现从传统的、基于直觉且往往容易产生偏差的方法到在无需预先了解药物功能基本原理的情况下预测化学性质的范式转变。这一观点突出了信息学在塑造药物化学领域中日益增长的重要性，特别是通过“信息团”的概念。信息团是指最小的化学结构，结合计算出的分子描述符、指纹以及其结构的机器学习表示，这些对于分子展现生物活性至关重要。类似于一把万能钥匙能打开多把锁，信息团指向触发生物反应的分子特征。通过对潜在先导化合物的超大数据集进行深入分析来识别和优化信息团，并在开发过程中实现标准部分的自动化，将大幅减少可能导致系统性错误的有偏差的直观决策，同时并行加速药物发现过程。
