Publications

27. Hussain, A., Shaw, P.E. & Hirst, J.D., Molecular dynamics simulations and in silico peptide ligand screening of the Elk-1 ETS domain. J. Cheminf., 3, 49 (2011).
DOI: http://dx.doi.org/10.1186/1758-2946-3-49
26. Turpin, E.R., Bonev, B.B., Hirst, J.D., Stereoselective disulfide formation stabilizes the local peptide conformation in Nisin mimics. Biochemistry, 49, 9594–9603 (2010).
DOI: http://dx.doi.org/10.1021/bi101214t
25. Hussain, A., Melville, J.L. & Hirst, J.D., Molecular docking and QSAR of aplyronine A and analogues: potent inhibitors of actin. J. Comput.-Aided Mol. Des., 24, 42005 (2010).
DOI: http://dx.doi.org/10.1007/s10822-009-9307-y
24. Spowage, B.M., Bruce, C.L. & Hirst, J.D., Interpretable correlation descriptors for quantitative structure-activity relationships. J. Cheminf, 1, 22 (2009).
DOI: http://dx.doi.org/10.1186/1758-2946-1-22

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23. Melville, J.L., Burke, E.K. & Hirst, J.D., Machine Learning in Virtual Screening. Comb. Chem. & High Thr. Scr., 12, 332–343 (2009).
22. Melville, J.L., Moal, I.H., Baker-Glenn, C., Shaw, P.E, Pattenden, G. & Hirst, J.D., The Structural Determinants of Macrolide-Actin Binding: In Silico Insights. Biophys. J., 92, 3862–3867 (2007).
DOI: http://dx.doi.org/10.1529/biophysj.106.103580
21. Melville, J.L. & Hirst, J.D., TMACC: Interpretable Correlation Descriptors for Quantitative Structure-Activity Relationships. J. Chem. Inf. Mod., 47, 626–634 (2007).
DOI: http://dx.doi.org/10.1021/ci6004178

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20. Vincent, E., Saxton, J., Baker-Glenn, C., Moal, I., Hirst, J.D., Pattenden, G. & Shaw, P.E., Disruption of actin dynamics and SRF-dependent gene regulation by the marine macrolide ulapualide A and its synthetic analogues. Cell. Mol. Life Sci., 64, 487–497 (2007).
DOI: http://dx.doi.org/10.1007/s00018-007-6427-1
19. Dryden, I.L., Hirst, J.D. & Melville, J.L., Statistical analysis of unlabelled points: comparing molecules in cheminformatics. Biometrics, 63, 237–251 (2007).
DOI: http://dx.doi.org/10.1111/j.1541-0420.2006.00622.x
18. Bruce, C.L., Melville, J.L., Pickett, S.D & Hirst, J.D., Contemporary QSAR Classifiers Compared. J. Chem. Inf. Mod., 47, 219–227 (2007).
DOI: http://dx.doi.org/10.1021/ci600332j

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17. Melville, J.L., Riley, J.F. & Hirst, J.D., Similarity by Compression. J. Chem. Inf. Mod., 47, 25–33 (2007).
DOI: http://dx.doi.org/10.1021/ci600384z

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16. Melville, J.L., Lovelock, K.J.R., Wilson, C., Allbutt, B., Burke, E.K., Lygo, B. & Hirst, J.D., Exploring Phase-Transfer Catalysis with Molecular Dynamics and 3D/4D Quantitative Structure-Selectivity Relationships. J. Chem. Inf. Mod., 45, 971–981 (2005).
DOI: http://dx.doi.org/10.1021/ci050051l
15. McNeany, T.J. & Hirst, J.D., Inhibition of the Tyrosine Kinase, Syk, Analyzed by Stepwise Non-Parametric Regression. J. Chem. Inf. Mod., 45, 768–776 (2005).
DOI: http://dx.doi.org/10.1021/ci049631t
14. Lygo, B., Andrews, B.I., Hirst, J.D., Melville, J.L., Peterson, J.A. & Slack, D., Rapid screening of cinchona alkaloid derived phase-transfer catalysts. Application in the optimization of a glycine imine alkylation. Chim Oggi, 22(9), 41920 (2004).
DOI: http://dx.doi.org/10.1002/chin.200549258
13. Melville, J.L. & Hirst, J.D., On the stability of CoMFA models. J. Chem. Inf. Comput. Sci., 44, 1294–1300 (2004).
DOI: http://dx.doi.org/10.1021/ci049944o
12. Melville, J.L., Andrews, B.I., Lygo, B. & Hirst, J.D., Computational screening of combinatorial catalyst libraries. Chem. Comm., 12, 1410–1411 (2004).
DOI: http://dx.doi.org/10.1039/b402378a

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11. Hirst, J.D., McNeany, T.J., Howe, T. & Whitehead, L., Application of Non- Parametric Regression to Quantitative Structure-Activity Relationships. Bioorg. Med. Chem., 10, 1037–1041 (2002).
DOI: http://dx.doi.org/10.1016/S0968-0896(01)00359-5

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10. Constans, P & Hirst, J.D., Non-Parametric Regressors Applied to Quantitative Structure-Activity Relationships. J. Chem. Inf. Comput. Sci., 40, 452–459 (2000).
DOI: http://dx.doi.org/10.1021/ci990082e
9. Hirst, J.D., Predicting Ligand Binding Energies. Curr. Opin. Drug Discovery & Development, 1, 28–33 (1998).
8. Vieth, M., Hirst, J.D., Dominy, B.N., Daigler, H. & Brooks III, C.L., Assessing Search Strategies for Flexible Docking. J. Comp. Chem., 19, 1623–1631 (1998).
DOI: http://dx.doi.org/10.1002/(SICI)1096-987X(19981115)19:14<1623::AID-JCC8>3.0.CO;2-L
7. Vieth, M., Hirst, J.D., Kolinski, A. & Brooks III, C.L., Assessing Energy Functions for Flexible Docking. J. Comp. Chem., 19, 1612–1622 (1998).
DOI: http://dx.doi.org/10.1002/(SICI)1096-987X(19981115)19:14<1612::AID-JCC7>3.0.CO;2-M
6. Vieth, M., Hirst, J.D. & Brooks III, C.L., Do Active Site Conformations of Small Ligands Correspond to Low Free Energy Structures? J. Comput.-Aided Mol. Des., 12, 563–572 (1998).
DOI: http://dx.doi.org/10.1023/A:1008055202136
5. Hirst, J.D., Nonlinear Quantitative Structure-Activity Relationship for the Inhibition of Dihydrofolate Reductase by Pyrimidines. J. Med. Chem., 39, 3526–3532 (1996).
DOI: http://dx.doi.org/10.1021/jm960197z
4. King, R.D., Hirst, J.D. & Sternberg, M.J.E., Drug Design by Machine Learning: A Comparative Study. Applications in Artificial Intelligence, 9, 213–233 (1995).
3. Hirst, J.D., King, R.D. & Sternberg, M.J.E., Quantitative Structure-Activity Relationships by Neural Networks and Inductive Logic Programming II. The Inhibition of Dihydrofolate Reductase by Triazines. J. Comput.-Aided Mol. Des., 8, 421–432 (1994).
DOI: http://dx.doi.org/10.1007/BF00125376
2. Hirst, J.D., King, R.D. & Sternberg, M.J.E., Quantitative Structure-Activity Relationships by Neural Networks and Inductive Logic Programming I. The Inhibition of Dihydrofolate Reductase by Pyrimidines. J. Comput.-Aided Mol. Des., 8, 405–420 (1994).
DOI: http://dx.doi.org/10.1007/BF00125375
1. King, R.D., Hirst, J.D. & Sternberg, M.J.E., New Approaches to QSAR: Neural Networks and Machine Learning. Perspectives in Drug Discovery and Design, 1, 279–290 (1993).
DOI: http://dx.doi.org/10.1007/BF02174529