A Comparative Assessment of Conventional and Machine-Learning-Based Scoring Functions in Predicting Binding Affinities of Protein-Ligand Complexes. - Hossam M. Ashtawy; Nihar R. Mahapatra (BIBM, 2011)

A Comparative Assessment of Ranking Accuracies of Conventional and Machine-Learning-Based Scoring Functions for Protein-Ligand Binding Affinity Prediction. - Hossam M. Ashtawy; Nihar R. Mahapatra (IEEE ACM Trans. Comput. Biol. Bioinform., 2012)

Molecular Docking for Drug Discovery: Machine-Learning Approaches for Native Pose Prediction of Protein-Ligand Complexes. - Hossam M. Ashtawy; Nihar R. Mahapatra (CIBB, 2013)

Does Accurate Scoring of Ligands against Protein Targets Mean Accurate Ranking? - Hossam M. Ashtawy; Nihar R. Mahapatra (ISBRA, 2013)

BgN-Score and BsN-Score: Bagging and boosting based ensemble neural networks scoring functions for accurate binding affinity prediction of protein-ligand complexes. - Hossam M. Ashtawy; Nihar R. Mahapatra (BMC Bioinform., 2015)

Machine-learning scoring functions for identifying native poses of ligands docked to known and novel proteins. - Hossam M. Ashtawy; Nihar R. Mahapatra (BMC Bioinform., 2015)

A Comparative Assessment of Predictive Accuracies of Conventional and Machine Learning Scoring Functions for Protein-Ligand Binding Affinity Prediction. - Hossam M. Ashtawy; Nihar R. Mahapatra (IEEE ACM Trans. Comput. Biol. Bioinform., 2015)

Boosted neural networks scoring functions for accurate ligand docking and ranking. - Hossam M. Ashtawy; Nihar R. Mahapatra (J. Bioinform. Comput. Biol., 2018)

Task-Specific Scoring Functions for Predicting Ligand Binding Poses and Affinity and for Screening Enrichment. - Hossam M. Ashtawy; Nihar R. Mahapatra (J. Chem. Inf. Model., 2018)

Descriptor Data Bank (DDB): A Cloud Platform for Multiperspective Modeling of Protein-Ligand Interactions. - Hossam M. Ashtawy; Nihar R. Mahapatra (J. Chem. Inf. Model., 2018)