COSMO-RS blind prediction of distribution coefficients and aqueous pKa values from the SAMPL8 challenge

IF 3 3区 生物学 Q3 BIOCHEMISTRY & MOLECULAR BIOLOGY
Michael Diedenhofen, Frank Eckert, Selman Terzi
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引用次数: 0

Abstract

The SAMPL8 blind prediction challenge, which addresses the acid/base dissociation constants (pKa) and the distribution coefficients (logD), was addressed by the Conductor like Screening Model for Realistic Solvation (COSMO-RS). Using the COSMOtherm implementation of COSMO-RS together with a rigorous conformational sampling, yielded logD predictions with a root mean square deviation (RMSD) of 1.36 log units over all 11 compounds and seven bi-phasic systems of the data set, which was the most accurate of all contest submissions (logD).

For the SAMPL8 pKa competition, participants were asked to report the standard state free energies of all microstates, which were then used to calculate the macroscopic pKa. We have used COSMO-RS based linear free energy fit models to calculate the requested energies. The assignment of the calculated and experimental pKa values was made on the basis of the popular transitions, i.e. the transition hat was predicted by the majority of the submissions. With this assignment and a model that covers both, pKa and base pKa, we achieved an RMSD of 3.44 log units (18 pKa values of 14 molecules), which is the second place of the six ranked submissions. By changing to an assignment that is based on the experimental transition curves, the RMSD reduces to 1.65. In addition to the ranked contribution, we submitted two more data sets, one for the standard pKa model and one or the standard base pKa model of COSMOtherm. Using the experiment based assignment with the predictions of the two sets we received a RMSD of 1.42 log units (25 pKa values of 20 molecules). The deviation mainly arises from a single outlier compound, the omission of which leads to an RMSD of 0.89 log units.

Abstract Image

COSMO-RS盲预测SAMPL8挑战的分布系数和水溶液pKa值
SAMPL8的盲预测挑战,即酸/碱解离常数(pKa)和分布系数(logD),是由导体筛选模型(cosmos - rs)解决的。使用COSMO-RS的COSMOtherm实现以及严格的构象抽样,对数据集的所有11种化合物和7种双相系统进行了logD预测,其均方根偏差(RMSD)为1.36 log单位,是所有参赛作品中最准确的(logD)。在SAMPL8 pKa竞赛中,参与者被要求报告所有微观状态的标准状态自由能,然后将其用于计算宏观pKa。我们使用基于cosmos - rs的线性自由能拟合模型来计算所需的能量。计算和实验pKa值的分配是基于流行的过渡,即大多数提交的预测的过渡。通过这项任务和一个涵盖pKa和碱基pKa的模型,我们实现了3.44 log单位的RMSD(18个pKa值为14个分子),这是六个排名提交的第二名。通过改变到一个基于实验过渡曲线的分配,RMSD减少到1.65。除了排名贡献外,我们还提交了另外两个数据集,一个用于标准pKa模型,一个用于COSMOtherm的标准基础pKa模型。使用基于实验的分配和两组预测,我们得到的RMSD为1.42 log单位(25 pKa值为20个分子)。偏差主要由单个异常值化合物引起,其遗漏导致RMSD为0.89 log单位。
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来源期刊
Journal of Computer-Aided Molecular Design
Journal of Computer-Aided Molecular Design 生物-计算机:跨学科应用
CiteScore
8.00
自引率
8.60%
发文量
56
审稿时长
3 months
期刊介绍: The Journal of Computer-Aided Molecular Design provides a form for disseminating information on both the theory and the application of computer-based methods in the analysis and design of molecules. The scope of the journal encompasses papers which report new and original research and applications in the following areas: - theoretical chemistry; - computational chemistry; - computer and molecular graphics; - molecular modeling; - protein engineering; - drug design; - expert systems; - general structure-property relationships; - molecular dynamics; - chemical database development and usage.
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