Molecular Gas-Phase Conformational Ensembles,Journal of Chemical Information and Modeling

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Molecular Gas-Phase Conformational Ensembles
Journal of Chemical Information and Modeling ( IF 5.6 ) Pub Date : 2024-01-11 , DOI: 10.1021/acs.jcim.3c01309
Susanta Das ₁ , Kenneth M Merz ₁

Affiliation

Accurately determining the global minima of a molecular structure is important in diverse scientific fields, including drug design, materials science, and chemical synthesis. Conformational search engines serve as valuable tools for exploring the extensive conformational space of molecules and for identifying energetically favorable conformations. In this study, we present a comparison of Auto3D, CREST, Balloon, and ETKDG (from RDKit), which are freely available conformational search engines, to evaluate their effectiveness in locating global minima. These engines employ distinct methodologies, including machine learning (ML) potential-based, semiempirical, and force field-based approaches. To validate these methods, we propose the use of collisional cross-section (CCS) values obtained from ion mobility–mass spectrometry studies. We hypothesize that experimental gas-phase CCS values can provide experimental evidence that we likely have the global minimum for a given molecule. To facilitate this effort, we used our gas-phase conformation library (GPCL) which currently consists of the full ensembles of 20 small molecules and can be used by the community to validate any conformational search engine. Further members of the GPCL can be readily created for any molecule of interest using our standard workflow used to compute CCS values, expanding the ability of the GPCL in validation exercises. These innovative validation techniques enhance our understanding of the conformational landscape and provide valuable insights into the performance of conformational generation engines. Our findings shed light on the strengths and limitations of each search engine, enabling informed decisions for their utilization in various scientific fields, where accurate molecular structure determination is crucial for understanding biological activity and designing targeted interventions. By facilitating the identification of reliable conformations, this study significantly contributes to enhancing the efficiency and accuracy of molecular structure determination, with particular focus on metabolite structure elucidation. The findings of this research also provide valuable insights for developing effective workflows for predicting the structures of unknown compounds with high precision.

中文翻译：

分子气相构象系综

准确确定分子结构的全局最小值在药物设计、材料科学和化学合成等不同科学领域中非常重要。构象搜索引擎是探索分子广泛构象空间和识别能量有利构象的宝贵工具。在本研究中，我们对 Auto3D、CREST、Balloon 和 ETKDG（来自 RDKit）这些免费的构象搜索引擎进行了比较，以评估它们在定位全局最小值方面的有效性。这些引擎采用不同的方法，包括基于机器学习 (ML) 势的方法、半经验方法和基于力场的方法。为了验证这些方法，我们建议使用从离子淌度-质谱研究中获得的碰撞截面（CCS）值。我们假设实验气相 CCS 值可以提供实验证据，证明我们可能具有给定分子的全局最小值。为了促进这项工作，我们使用了气相构象库 (GPCL)，该库目前由 20 个小分子的完整集合组成，社区可以使用它来验证任何构象搜索引擎。使用我们用于计算 CCS 值的标准工作流程，可以为任何感兴趣的分子轻松创建 GPCL 的更多成员，从而扩展 GPCL 在验证练习中的能力。这些创新的验证技术增强了我们对构象景观的理解，并为构象生成引擎的性能提供了宝贵的见解。我们的研究结果揭示了每个搜索引擎的优势和局限性，使其能够在各个科学领域中做出明智的利用决策，其中准确的分子结构测定对于理解生物活性和设计有针对性的干预措施至关重要。通过促进可靠构象的识别，这项研究极大地有助于提高分子结构测定的效率和准确性，特别是代谢物结构的阐明。这项研究的结果还为开发有效的工作流程以高精度预测未知化合物的结构提供了宝贵的见解。

更新日期：2024-01-11

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