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Machine-Learning-Enabled Virtual Screening for Inhibitors of Lysine-Specific Histone Demethylase 1 (2021)
Journal Article
Zhou, J., Wu, S., Lee, B. G., Chen, T., He, Z., Lei, Y., …Hirst, J. D. (2021). Machine-Learning-Enabled Virtual Screening for Inhibitors of Lysine-Specific Histone Demethylase 1. Molecules, 26(24), Article 7492. https://doi.org/10.3390/molecules26247492

A machine learning approach has been applied to virtual screening for lysine specific demethylase 1 (LSD1) inhibitors. LSD1 is an important anti-cancer target. Machine learning models to predict activity were constructed using Morgan molecular finger... Read More about Machine-Learning-Enabled Virtual Screening for Inhibitors of Lysine-Specific Histone Demethylase 1.

Erratum: Vibrational Spectroscopic Map, Vibrational Spectroscopy, and Intermolecular Interaction (Chemical Reviews (2020) 120:15 (7152−7218) DOI: 10.1021/acs.chemrev.9b00813) (2021)
Journal Article
Baiz, C. R., Błasiak, B., Bredenbeck, J., Cho, M., Choi, J. H., Corcelli, S. A., …Zanni, M. T. (2021). Erratum: Vibrational Spectroscopic Map, Vibrational Spectroscopy, and Intermolecular Interaction (Chemical Reviews (2020) 120:15 (7152−7218) DOI: 10.1021/acs.chemrev.9b00813). Chemical Reviews, 121(21), 13698. https://doi.org/10.1021/acs.chemrev.1c00758

The authors make the following additions and corrections to the paper, C. Baiz et al., Vibrational Spectroscopic Map, Vibrational Spectroscopy, and Intermolecular Interaction. Chem. Rev. 2020, 120, 7152?7218. 1. The below funding sources should be ad... Read More about Erratum: Vibrational Spectroscopic Map, Vibrational Spectroscopy, and Intermolecular Interaction (Chemical Reviews (2020) 120:15 (7152−7218) DOI: 10.1021/acs.chemrev.9b00813).

Kernel Methods for Predicting Yields of Chemical Reactions (2021)
Journal Article
Haywood, A. L., Redshaw, J., Hanson-Heine, M. W., Taylor, A., Brown, A., Mason, A. M., …Hirst, J. D. (2022). Kernel Methods for Predicting Yields of Chemical Reactions. Journal of Chemical Information and Modeling, 62(9), 2077-2092. https://doi.org/10.1021/acs.jcim.1c00699

The use of machine learning methods for the prediction of reaction yield is an emerging area. We demonstrate the applicability of support vector regression (SVR) for predicting reaction yields, using combinatorial data. Molecular descriptors used in... Read More about Kernel Methods for Predicting Yields of Chemical Reactions.

Benzene, Toluene, and Monosubstituted Derivatives: Diabatic Nature of the Oscillator Strengths of S1 ← S0 Transitions (2021)
Journal Article
Robinson, D., Alarfaji, S. S., & Hirst, J. D. (2021). Benzene, Toluene, and Monosubstituted Derivatives: Diabatic Nature of the Oscillator Strengths of S1 ← S0 Transitions. Journal of Physical Chemistry A, 125(24), 5237–5245. https://doi.org/10.1021/acs.jpca.1c01685

For benzene, toluene, aniline, fluorobenzene, and phenol, even sophisticated treatments of electron correlation, such as MRCI and XMS-CASPT2 calculations, show oscillator strengths typically lower than experiment. Inclusion of a simple pseudo-diabati... Read More about Benzene, Toluene, and Monosubstituted Derivatives: Diabatic Nature of the Oscillator Strengths of S1 ← S0 Transitions.

Near-ultraviolet circular dichroism and two-dimensional spectroscopy of polypeptides (2021)
Journal Article
Segatta, F., Rogers, D. M., Dyer, N. T., Guest, E. E., Li, Z., Do, H., …Hirst, J. D. (2021). Near-ultraviolet circular dichroism and two-dimensional spectroscopy of polypeptides. Molecules, 26(2), Article 396. https://doi.org/10.3390/MOLECULES26020396

A fully quantitative theory of the relationship between protein conformation and optical spectroscopy would facilitate deeper insights into biophysical and simulation studies of protein dynamics and folding. In contrast to intense bands in the far-ul... Read More about Near-ultraviolet circular dichroism and two-dimensional spectroscopy of polypeptides.