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Publications

  • H. M. Ashtawy and N. R. Mahapatra. Boosted neural networks scoring functions for accurate ligand docking and ranking. Journal of Bioinformatics and Computational Biology (JBCB), 16(2):1850004, 2018.
  • H. M. Ashtawy and N. R. Mahapatra. Descriptor Data Bank (DDB): A Cloud Platform for Multiperspective Modeling of Protein–Ligand Interactions. Journal of chemical information and modeling (JCIM), ACS, 58 (1): pp 134–147, 2017.
  • H. M. Ashtawy and N. R. Mahapatra. Task-Specific Scoring Functions for Predicting Ligand Binding Poses and Affinity and for Screening Enrichment. Journal of chemical information and modeling (JCIM), ACS, 58 (1): pp 119–133, 2017.
  • H. M. Ashtawy and N. R. Mahapatra. BgN-Score and BsN-Score: Bagging and boosting based ensemble neural networks scoring functions for accurate binding affinity prediction of protein-ligand complexes. BMC bioinformatics, 16(Suppl 4):S8, 2015.
  • H. M. Ashtawy and N. R. Mahapatra. Machine-learning scoring functions for identifying native poses of ligands docked to known and novel proteins. BMC Bioinformatics, 16(Suppl 6):S3, 2015.
  • H. M. Ashtawy and N. R. Mahapatra. Ensemble neural networks scoring functions for accurate binding affinity prediction of protein-ligand complexes. In Pattern Recognition in Bioinformatics: 9th IAPR International Conference, PRIB 2014, Stockholm, Sweden, August 21-23, 2014. Proceedings, volume 8626, page 129. Springer, 2014.
  • H. M. Ashtawy and N. R. Mahapatra. A comparative assessment of predictive accuracies of conventional and machine learning scoring functions for protein-ligand binding affinity prediction. Computational Biology and Bioinformatics, IEEE/ACM Transactions on, PP(99):1–1, 2014.
  • H. M. Ashtawy and N. R. Mahapatra. Molecular docking for drug discovery: Machine-learning approaches for native pose prediction of protein-ligand complexes. In Computational Intelligence Methods for Bioinformatics and Biostatistics, pages 15–32. Springer, 2014.
  • H. M. Ashtawy and N. R. Mahapatra. Does accurate scoring of ligands against protein targets mean accurate ranking? In Proc. 9th International Symposium on Bioinformatics Research and Applications (ISBRA 2013), pages 298–310. Springer, 2013.
  • H. M. Ashtawy and N. R. Mahapatra. Enn-score: An ensemble neural networks scoring function for accurate binding affinity prediction of protein-ligand complexes. In Proc. 9th International Symposium on Bioinformatics Research and Applications (ISBRA 2013), pages 54–61, 2013.
  • H. M. Ashtawy and N. R. Mahapatra. A comparative assessment of ranking accuracies of conventional and machine-learning-based scoring functions for protein-ligand binding affinity prediction. IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), 9(5):1301–1313, 2012.
  • H. M. Ashtawy and N. R. Mahapatra. A comparative assessment of ranking powers of conventional and machine-learning-based scoring functions on diverse and homogeneous test sets. In Proc. 10th Asia-Pacific Bioinformatics Conference (APBC 2012), pages 241–254, 2012.
  •  H. M. Ashtawy and N. R. Mahapatra. A comparative assessment of conventional and machine-learning-based scoring functions in predicting binding affinities of protein-ligand complexes. In Bioinformatics and Biomedicine (BIBM), 2011 IEEE International Conference on, pages 627–630. IEEE, 2011.

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