iSIM:即时相似性

IF 6.2 Q1 CHEMISTRY, MULTIDISCIPLINARY Digital discovery Pub Date : 2024-05-07 DOI:10.1039/D4DD00041B
Kenneth López-Pérez, Taewon D. Kim and Ramón Alain Miranda-Quintana
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引用次数: 0

摘要

自化学信息学诞生之初,分子相似性的量化问题就一直存在。尽管已经报道了多种相似性指数和分子表示方法,但所有这些方法最终都只能一次计算两个对象的分子相似性。因此,要得到一组分子的平均相似性,就需要计算所有成对比较,这就要求计算资源的数量按二次方缩放。iSIM 可同时对多个分子进行比较,并得出与用二进制指纹和实值描述符表示的分子成对比较平均值相同的值。在这项工作中,我们将介绍 iSIM 的数学框架以及在化学取样、可视化、多样性选择和聚类方面的若干应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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iSIM: instant similarity†

The quantification of molecular similarity has been present since the beginning of cheminformatics. Although several similarity indices and molecular representations have been reported, all of them ultimately reduce to the calculation of molecular similarities of only two objects at a time. Hence, to obtain the average similarity of a set of molecules, all the pairwise comparisons need to be computed, which demands a quadratic scaling in the number of computational resources. Here we propose an exact alternative to this problem: iSIM (instant similarity). iSIM performs comparisons of multiple molecules at the same time and yields the same value as the average pairwise comparisons of molecules represented by binary fingerprints and real-value descriptors. In this work, we introduce the mathematical framework and several applications of iSIM in chemical sampling, visualization, diversity selection, and clustering.

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Back cover ArcaNN: automated enhanced sampling generation of training sets for chemically reactive machine learning interatomic potentials. Sorting polyolefins with near-infrared spectroscopy: identification of optimal data analysis pipelines and machine learning classifiers†‡ High accuracy uncertainty-aware interatomic force modeling with equivariant Bayesian neural networks† Correction: A smile is all you need: predicting limiting activity coefficients from SMILES with natural language processing
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