The Grimme-D3 semi-empirical dispersion energy correction has been implemented for the original effective fragment potential for water (EFP1), and for systems that contain water molecules described by both correlated ab initio quantum mechanical (QM) molecules and EFP1. Binding energies obtained with these EFP1-D and QM/EFP1-D methods were tested using 27 benchmark species, including neutral, protonated, deprotonated, and autoionized water clusters and nine solute-water binary complexes. The EFP1-D and QM/EFP1-D binding energies are compared with those obtained using fully QM methods: second-order perturbation theory, and coupled cluster theory, CCSD(T), at the complete basis set (CBS) limit. The results show that the EFP1-D and QM/EFP1-D binding energies are in good agreement with CCSD(T)/CBS binding energies with a mean absolute error of 5.9 kcal/mol for water clusters and 0.8 kcal/mol for solute-water binary complexes.

Rapid and accurate identification of unknown compounds within suspicious samples confiscated for sports doping control and law enforcement drug testing is critical, but such analyses are often conducted manually and can be time-consuming. Here, we report a methodology for automated identification of unknown substances in confiscation samples by rapid automatic flow-injection analysis on a liquid chromatography coupled to high-resolution mass spectrometry system and identifying unknown compounds with Compound Discoverer software. The developed methodology was validated by comparing the automated identification results with those obtained from manual syringe-infusion experiments and manual tandem mass spectral library searches. The automated methodology resulted in far higher throughput and remarkably shorter turnaround time for analysis when compared with manual procedures and, in most cases, yielded more compounds. As this is the first such report to the authors' knowledge, this methodology may potentially transform analysis of confiscated samples in sports doping control and law enforcement drug testing.

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