Virtual screening is an effective way to find hits in drug discovery, with approaches ranging from fast information-based similarity methods to more computationally intensive physics-based docking methods. However, the best approach to use for a given project is not clear in advance of the screen. In this work, we show that combining results from multiple methods using a standard score (Z-score) can significantly improve virtual screening enrichments over any of the single screening methods. We show that an augmented Z-score, which considers the best two out of three scores for a given compound, outperforms previously published data fusion algorithms. We use three different virtual screening methods (two-dimensional (2D) fingerprint similarity, shape-based similarity, and docking) and study two different databases (DUD and MDDR). The average enrichment in the top 1% was improved by 9% for DUD and 25% for the MDDR, compared with the top individual method. Improvements of 22% for DUD and 43% for MDDR are seen over the average of the three individual methods. Statistics are presented that show a high significance associated with the findings in this work.
Field-effect transistor (FET)-based microelectronics is approaching its size limit due to unacceptable power dissipation and short-channel effects. Molecular quantum dot cellular automata (MQCA) is a promising transistorless paradigm that encodes binary information with bistable charge configurations instead of currents and voltages. However, it still remains a challenge to find appropriate candidate molecules for MQCA operation. Inspired by recent progress in boron radical chemistry, we theoretically predicted a series of new MQCA candidates built from diboryl monoradical anions. The unpaired electron resides mainly on one boron center and can be shifted to the other by an electrostatic stimulus, forming bistable charge configurations required by MQCA. By investigating various bridge units with different substitutions (ortho-, meta-, and para-), we suggested several candidate molecules that have potential MQCA applications.
Computational vibrational spectroscopy serves as an important tool in the interpretation of experimental infrared (IR) spectra. In this article, we present a systematic benchmarking study of DFTB3 with two different computational vibrational spectroscopic methods, based on either normal mode analysis (NMA) or fast Fourier transform dipole autocorrelation function (FT-DAC). The results were compared with experimental data and theoretical calculations with B3LYP/cc-pVTZ. The empirical scaling factors for DFTB3/NMA, DFTB3-freq/NMA, and DFTB3/FT-DAC methods are 0.9993, 1.0059, and 0.9982, respectively. We also demonstrate the significance of anharmonicity and conformational sampling in vibrational spectroscopic calculations on flexible molecules. As expected, DFTB3/FT-DAC predicted the anharmonic vibrational peaks more accurately than DFTB3/NMA and NMA spectra are highly dependent on the initial structures. The potential limitations of DFTB3 for vibrational spectroscopic calculations and the challenges in assigning the FT-DAC spectral peaks were noted. DFTB3/FT-DAC is expected to serve as a promising technique in computational spectroscopy in complex biomolecular systems.
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