Instituto de Química Rosario (CONICET), Facultad de Ciencias Bioquímicas y Farmacéuticas, Universidad Nacional de Rosario, Suipacha 531, Rosario 2000, Argentina.
Instituto de Investigaciones en Ingeniería Ambiental, Química y Biotecnología Aplicada (INGEBIO), Facultad de Química e Ingeniería del Rosario, Pontificia Universidad Católica Argentina, S2002QEO Rosario, Argentina.
Org Lett. 2022 Oct 21;24(41):7487-7491. doi: 10.1021/acs.orglett.2c01251. Epub 2022 May 4.
A new tool, ML--DP4, provides an efficient and accurate method for determining the most likely structure of complex molecules within minutes using standard computational resources. The workflow involves combining fast Karplus-type calculations with NMR chemical shifts predictions at the cheapest HF/STO-3G level enhanced using machine learning (ML), all embedded in the -DP4 formalism. Our ML provides accurate predictions, which compare favorably alongside with other ML methods.
一种新工具 ML--DP4 提供了一种高效准确的方法,可以在几分钟内使用标准计算资源确定复杂分子最可能的结构。该工作流程涉及将快速的 Karplus 类型计算与使用机器学习 (ML) 在最便宜的 HF/STO-3G 水平上进行的 NMR 化学位移预测相结合,所有这些都嵌入在 -DP4 形式中。我们的 ML 提供了准确的预测,与其他 ML 方法相比具有优势。