Jones, D. T., & Kandathil, S. M. (2018). High precision in protein contact prediction using fully convolutional neural networks and minimal sequence features. Bioinformatics. doi:10.1093/bioinformatics/bty341

Kandathil, S. M., Garza-Fabre, M., Handl, J., & Lovell, S. C. (2018). Improved fragment-based protein structure prediction by redesign of search heuristics. Scientific Reports, 8, 13694. doi:10.1038/s41598-018-31891-8

Olabode, A. S., Kandathil, S. M., Lovell, S. C., & Robertson, D. L. (2017). Adaptive HIV-1 evolutionary trajectories are constrained by protein stability. Virus Evolution, 3 (2). doi:10.1093/ve/vex019

Handl, J., Garza-Fabre, M., Kandathil, S. M., & Lovell, S. C. (2017). On heuristic bias in fragment-assembly methods for protein structure prediction. GECCO (Companion), 1652-1656. ACM. doi:10.1145/3067695.3082545

Garza-Fabre, M., Kandathil, S. M., Handl, J., Knowles, J., & Lovell, S. C. (2016). Generating, Maintaining, and Exploiting Diversity in a Memetic Algorithm for Protein Structure Prediction. Evolutionary Computation, 24 (4), 577-607. doi:10.1162/EVCO_a_00176

Kandathil, S. M., Handl, J., & Lovell, S. C. (2016). Toward a detailed understanding of search trajectories in fragment assembly approaches to protein structure prediction. Proteins: Structure, Function, and Bioinformatics, 84 (4), 411-426. doi:10.1002/prot.24987

Hughes, T. J., Kandathil, S. M., & Popelier, P. L. A. (2015). Accurate prediction of polarised high order electrostatic interactions for hydrogen bonded complexes using the machine learning method kriging. SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY, 136, 32-41. doi:10.1016/j.saa.2013.10.059

Garza-Fabre, M., Kandathil, S. M., Handl, J., Knowles, J., & Lovell, S. C. (2015). Using machine learning to explore the relevance of local and global features during conformational search in Rosetta. GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference, 935-938. doi:10.1145/2739482.2768441

Kandathil, S. M., Driscoll, M. D., Dunn, R. V., Scrutton, N. S., & Hay, S. (2014). Proton tunnelling and promoting vibrations during the oxidation of ascorbate by ferricyanide?. PHYSICAL CHEMISTRY CHEMICAL PHYSICS, 16 (6), 2256-2259. doi:10.1039/c3cp55131h

Fletcher, T. L., Kandathil, S. M., & Popelier, P. L. A. (2014). The prediction of atomic kinetic energies from coordinates of surrounding atoms using kriging machine learning. THEORETICAL CHEMISTRY ACCOUNTS, 133 (7), ARTN 1499. doi:10.1007/s00214-014-1499-0