Computational predictions of cocrystal formation: A benchmark study of 28 assemblies comparing five methods from high-throughput to advanced models

IF 3.4 3区 化学 Q2 CHEMISTRY, MULTIDISCIPLINARY Journal of Computational Chemistry Pub Date : 2024-07-03 DOI:10.1002/jcc.27454
Robert Fox, Joaquin Klug, Damien Thompson, Anthony Reilly
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Abstract

Cocrystals are assemblies of more than one type of molecule stabilized through noncovalent interactions. They are promising materials for improved drug formulation in which the stability, solubility, or biocompatibility of the active pharmaceutical ingredient (API) is improved by including a coformer. In this work, a range of density functional theory (DFT) and density functional tight binding (DFTB) models are systematically compared for their ability to predict the lattice enthalpy of a broad range of existing pharmaceutically relevant cocrystals. These range from cocrystals containing model compounds 4,4′-bipyridine and oxalic acid to those with the well benchmarked APIs of aspirin and paracetamol, all tested with a large set of alternative coformers. For simple cocrystals, there is a general consensus in lattice enthalpy calculated by the different DFT models. For the cocrystals with API coformers the cocrystals, enthalpy predictions depend strongly on the DFT model. The significantly lighter DFTB models predict unrealistic values of lattice enthalpy even for simple cocrystals.

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共晶体形成的计算预测:对从高通量到高级模型的五种方法进行比较的 28 种组合的基准研究。
共晶体是一种以上的分子通过非共价相互作用而稳定的集合体。它们是一种很有前景的改良药物制剂材料,通过加入共形物可以提高活性药物成分(API)的稳定性、溶解性或生物相容性。在这项研究中,我们对一系列密度泛函理论(DFT)和密度泛函紧密结合(DFTB)模型进行了系统比较,以了解它们预测多种现有药物相关共晶体晶格焓的能力。这些共晶体包括含有模型化合物 4,4'-联吡啶和草酸的共晶体,以及含有阿司匹林和扑热息痛等基准原料药的共晶体。对于简单的共晶体,不同 DFT 模型计算出的晶格焓基本一致。对于含有原料药共聚物的共晶体,焓的预测结果在很大程度上取决于 DFT 模型。即使对于简单的共晶体,明显较轻的 DFTB 模型也能预测出不切实际的晶格焓值。
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来源期刊
CiteScore
6.60
自引率
3.30%
发文量
247
审稿时长
1.7 months
期刊介绍: This distinguished journal publishes articles concerned with all aspects of computational chemistry: analytical, biological, inorganic, organic, physical, and materials. The Journal of Computational Chemistry presents original research, contemporary developments in theory and methodology, and state-of-the-art applications. Computational areas that are featured in the journal include ab initio and semiempirical quantum mechanics, density functional theory, molecular mechanics, molecular dynamics, statistical mechanics, cheminformatics, biomolecular structure prediction, molecular design, and bioinformatics.
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