PUBLICATIONS

NB: Last updated on May 2017 (5 more papers in revision). Corresponding author/s are in bold font. Citations can be found here.

  1. Matthias Ziehm, Satwant Kaur, Dobril K. Ivanov, Roman A. Laskowski, Pedro J. Ballester, David Marcus, Linda Partridge, Janet M. Thornton (2017) “Drug repurposing for ageing research using model organisms”. Aging Cell (In Press).
  2. Wójcikowski, M., Ballester, P.J., Siedlecki, P. (2017) “Performance of machine-learning scoring functions in structure-based virtual screening”. Scientific Reports 7, 46710. [PDF]
  3. Morro, Antoni; Canals, Vincent; Oliver, Antoni; Alomar, Miquel; Galan-Prado, Fabio; Ballester, P.J., Rossello, Josep (2017) “A Stochastic Spiking Neural Network for Virtual Screening”. IEEE Transactions on Neural Networks and Learning Systems 99, 1-5. [PDF]
  4. Guinney, J. et al. (2017) “Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open clinical trial data”. Lancet Oncology 18, No. 1, 132–142. [PDF]
  5. Nguyen, L., Dang, C., Ballester, P.J. (2016) “Systematic assessment of multi-gene predictors of pan-cancer cell line sensitivity to drugs exploiting gene expression data”. F1000Research 5(ISCB Comm J):2927. [PDF]
  6. Li, H., Leung, K.-S., Wong, M.-H., Ballester, P.J. (2016) “Correcting the impact of docking pose generation error on binding affinity prediction”. BMC Bioinformatics 17(Suppl 11):308. [PDF]
  7. Li, H., Leung, K.-S., Wong, M.-H., Ballester, P.J. (2016) “USR-VS: a web server for large-scale prospective virtual screening using ultrafast shape recognition techniques”. Nucleic Acid Research 44 (W1): W436-W441. [PDF]
  8. Peon, A., Dang, C., Ballester, P.J. (2016) “How reliable are ligand-centric methods for target fishing?”. Frontiers in Chemistry 4:15. [PDF]
  9. Ain, Q.U., Aleksandrova, A., Roessler, F.D., Ballester, P.J. (2015) “Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening”. WIREs Computational Molecular Science 5, 405–424. [PDF]
  10. Li, H., Leung, K.-S., Wong, M.-H., Ballester, P.J. (2015) “The impact of docked pose generation error on the accuracy of machine-learning scoring functions”. Computational Intelligence Methods for Bioinformatics and Biostatistics: Lecture Notes in Bioinformatics 8623, 231–241. [PDF]
  11. Li, H., Leung, K.-S., Wong, M.-H., Ballester, P.J. (2015) “The importance of the regression model in the prediction of intermolecular binding using AutoDock Vina”. Computational Intelligence Methods for Bioinformatics and Biostatistics: Lecture Notes in Bioinformatics 8623, 219–230. [PDF]
  12. Li, H., Leung, K.-S., Wong, M.-H., Ballester, P.J. (2015) “Low-quality structural and interaction data improves binding affinity prediction via random forest”. Molecules 20, 10947-10962. [PDF]
  13. Li, H., Leung, K.-S., Wong, M.-H., Ballester, P.J. (2015) “The use of Random Forest to predict binding affinity in docking”. Bioinformatics and Biomedical Engineering: Lecture Notes in Computer Science 9044, 238-247. [PDF]
  14. Li, H., Leung, K.-S., Wong, M.-H., Ballester, P.J. (2015) “Improving AutoDock Vina using Random Forest: the growing accuracy of binding affinity prediction by the effective exploitation of larger data sets”. Molecular Informatics 34, 115-126. [PDF]
  15. Li, H., Leung, K.-S., Wong, M.-H., Ballester, P.J. (2014) “Substituting random forest for multiple linear regression improves binding affinity prediction of scoring functions: cyscore as a case study”. BMC Bioinformatics 15:291 [PDF]
  16. Hoeger, B., Diether, M., Ballester, P.J., Köhn, M. (2014) “Biochemical evaluation of new virtual screening methods reveals cell-active inhibitors of the cancer-promoting phosphatases of regenerating liver”. European Journal of Medicinal Chemistry 88, 89-100. [PDF]
  17. Ballester, P.J., Schreyer, A., Blundell, T.L. (2014) ‘Does a more precise chemical description of protein-ligand complexes lead to more accurate prediction of binding affinity?’. Journal of Chemical Information and Modeling 54:944-55 [PDF]
  18. Patil, S.P., Ballester, P.J., Kerezsi, C. (2014) ‘Prospective Virtual Screening for Novel p53-MDM2 Inhibitors Using Ultrafast Shape Recognition’. Journal of Computer-Aided Molecular Design 28:89-97 [PDF]
  19. Li H., Leung K.-S., Ballester P.J., Wong M.-H. (2014) ‘istar: A Web Platform for Large-Scale Online Protein-Ligand Docking’. PLoS ONE 9(1): e85678 [PDF]
  20. Teo, C.Y., Rahman, M.B.A., Chor, A.L.T., Salleh, A.B., Ballester, P.J., Tejo, B. (2013) ‘Ligand-Based Virtual Screening for the Discovery of Inhibitors for Protein Arginine Deiminase Type 4 (PAD4)’. Metabolomics 3:118, 1-5 [PDF].
  21. Menden, M., Iorio, F., Garnett, M., McDermott, U., Benes, C., Ballester, P.J., Saez-Rodriguez, J. (2013): ‘Machine Learning Prediction of Cancer Cell Sensitivity to Drugs Based on Genomic and Chemical Properties’ PLoS ONE 8, e61318 [PDF]
  22. Ballester, P.J. (2012): ‘Machine Learning Scoring Functions based on Random Forest and Support Vector Regression’. PRIB 2012, Lecture Notes in Bioinformatics Series 7632, Springer, pp. 14-25.
  23. Ballester, P.J., Mangold, M., Howard, N.I., Marchese-Robinson, R.L., Abell, C., Blumberger, J. and Mitchell, J.B.O. (2012): `Hierarchical virtual screening for the discovery of new molecular scaffolds in antibacterial hit identification’. Journal of the Royal Society Interface [PDF]
  24. Ballester, P.J. and Brown, N. (2012): ‘Molecular shape’. Bioisosteres in Medicinal Chemistry. Series: Methods and Principles in Medicinal Chemistry. Wiley-VCH [LINK]
  25. Ballester, P.J. and Mitchell, J.B.O. (2011):‘Comments on “Leave-Cluster-Out Cross-Validation is appropriate for scoring functions derived from diverse protein data sets”: significance for the validation of scoring functions’. Journal of Chemical Information and Modeling 51 (8), 1739–1741 [PDF]
  26. Ballester, P.J. (2011): ‘Ultrafast shape recognition: method and applications’. Future Medicinal Chemistry 3 (1), 65-78 [PDF][PREPRINT]
  27. Ballester, P.J. and Mitchell, J.B.O. (2010): ‘A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking’. Bioinformatics 26 (9), 1169-1175[PDF][SD]
  28. Ballester, P.J., Westwood, I., Laurieri, N., Sim, E. and Richards, W.G. (2010): ‘Prospective virtual screening with Ultrafast Shape Recognition: the identification of novel inhibitors of arylamine N-acetyltransferases’. Journal of the Royal Society Interface 7, 335-342 [PDF][SD]
  29. Ballester, P.J., Finn, P.W. and Richards, W.G. (2009): ‘Ultrafast Shape Recognition: Evaluating a new ligand-based virtual screening technology’. Journal of Molecular Graphics and Modelling 27, 836-845 [PDF]
  30. Ballester, P.J. and Richards, W.G. (2007):’Ultrafast Shape Recognition for Similarity Search in Molecular Databases’. Proceedings of the Royal Society A 463, 1307-1321 [PDF]
  31. Ballester, P.J. and Carter, J.N. (2007): ‘Model Calibration of a Real Petroleum Reservoir using a Parallel Real-coded Genetic Algorithm’. Proceedings of the 2007 IEEE Congress on Evolutionary Computation, IEEE Press, 4313-4320 [PDF]
  32. Ballester, P.J. and Richards, W.G. (2007):‘Ultrafast Shape Recognition to Search Compound Databases for Similar Molecular Shapes’. Journal of Computational Chemistry 28 (10), 1711-1723 [PDF]
  33. Ballester, P.J. and Carter, J.N. (2007): ‘A Parallel Real-coded Genetic Algorithm for History Matching and its Application to a Real Petroleum Reservoir’. Journal of Petroleum Science and Engineering 59, 157-168 [PDF]
  34. Ballester, P.J. and Richards, W.G. (2006): ‘A Multiparent Version of the Parent Centric Crossover for Multimodal Optimization’. Proceedings of the 2006 IEEE World Congress on Computational Intelligence, IEEE Press, 10356-10363 [PDF]
  35. Carter, J.N., Ballester, P.J., Tavassoli, Z. and King, P.R. (2006): ‘Our Calibrated Model has Poor Predictive Value: An Example from the Petroleum Industry’. Reliability Engineering and System Safety 91, 1373-1381 [PDF]
  36. Ballester, P.J. and Carter, J.N. (2006):‘Characterising the Parameter Space of a Highly Nonlinear Inverse Problem’. Inverse Problems in Science and Engineering 14 (2), 171-191 [PDF]
  37. Ballester, P.J., Stephenson, J., Carter, J.N. and Gallagher, K. (2005):‘Real-Parameter Optimisation Performance Study on the CEC-2005 benchmark with SPC-PNX’. Proceedings of the 2005 IEEE Congress on Evolutionary Computation, Vol. 1, IEEE Press, 498-505 [PDF]
  38. Gallagher, K., Stephenson, J., Brown R., Holmes, C. and Ballester, P. (2005): ‘Exploiting 3-D spatial sampling in modeling of thermochronological data’. Reviews in Mineralogy and Geochemistry 58, 375-387 [PDF]
  39. Ballester, P.J. and Carter, J.N. (2004): ‘An Effective Real-Parameter Genetic Algorithms with Parent Centric Normal Crossover for Multimodal Optimization’. Lecture Notes in Computer Science 3102, Springer, 901-913 [PDF]
  40. Ballester, P.J. and Carter, J.N. (2004):‘Tackling an Inverse Problem from the Petroleum Industry with a Genetic Algorithm for Sampling’. Lecture Notes in Computer Science 3103, Springer, 1299-1300 [PDF]
  41. Carter, J.N., Ballester, P.J., Tavassoli, Z. and King, P.R. (2004): ‘Our Calibrated Model has no Predictive Value: An Example from the Petroleum Industry’. Proceedings of the Fourth International Conference on Sensitivity Analysis of Model Output (SAMO-04, Santa Fe, USA), Los Alamos National Laboratory, 194-200 [LINK]
  42. Ballester, P.J. and Carter, J.N. (2004): ‘An Effective Real-Parameter Genetic Algorithms for Multimodal Optimization’. Sixth International Conference on Adaptive Computing in Design and Manufacture (ACDM-04, Bristol, UK). Adaptive Computing in Design and Manufacture VI. I.C. Parmee (Ed.), Springer, 359-364 [LINK]
  43. Ballester, P.J. and Carter, J.N. (2004): ‘An Algorithm to Identify Clusters of Solutions in Multimodal Optimisation’. Lecture Notes in Computer Science 3059, Springer, 42-56 [PDF]
  44. Carter, J.N. and Ballester, P.J. (2004): ‘A Real Parameter Genetic Algorithm for Cluster Identification in History Matching’. Ninth European Conference on the Mathematics of Oil Recovery (ECMOR-IX, Cannes, France). EAGE Publications BV. A012, 1-8 [LINK]
  45. Ballester, P.J. and Carter, J.N. (2003): ‘Real-Parameter Genetic Algorithms for Finding Multiple Optimal Solutions in Multi-modal Optimization’. Lecture Notes in Computer Science 2723, Springer, 706-717 [PDF]

Selected Technical Reports

Ballester, P.J. (2005): ‘New Computational Methods to Address Nonlinear Inverse Problems’. PhD Thesis, Faculty of Engineering, Imperial College London [LINK]

Ballester, P.J. (2001): “A neural network based prediction method for burst detection.” MSc Thesis, Department of Mathematics, King’s College London.

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