Alpha 2.0
Login Register
» Authors » Nihar R Mahapatra

Nihar R Mahapatra

Explore the profile of Nihar R Mahapatra including associated specialties, affiliations and a list of published articles. Areas
Snapshot
Articles 7
Citations 101
Followers 0
Related Specialties
Biology
Chemistry
Medical Informatics
Co-Authors
Hossam M Ashtawy
Published In
IEEE/ACM Trans Comput Biol Bioinform
BMC Bioinformatics
J Chem Inf Model
J Bioinform Comput Biol
Affiliations
Soon will be listed here.
Recent Articles
1.
Boosted neural networks scoring functions for accurate ligand docking and ranking
Ashtawy H, Mahapatra N
J Bioinform Comput Biol . 2018 Mar; 16(2):1850004. PMID: 29495922
Predicting the native poses of ligands correctly is one of the most important steps towards successful structure-based drug design. Binding affinities (BAs) estimated by traditional scoring functions (SFs) are typically...
2.
Task-Specific Scoring Functions for Predicting Ligand Binding Poses and Affinity and for Screening Enrichment
Ashtawy H, Mahapatra N
J Chem Inf Model . 2017 Dec; 58(1):119-133. PMID: 29190087
Molecular docking, scoring, and virtual screening play an increasingly important role in computer-aided drug discovery. Scoring functions (SFs) are typically employed to predict the binding conformation (docking task), binding affinity...
3.
Descriptor Data Bank (DDB): A Cloud Platform for Multiperspective Modeling of Protein-Ligand Interactions
Ashtawy H, Mahapatra N
J Chem Inf Model . 2017 Dec; 58(1):134-147. PMID: 29186950
Protein-ligand (PL) interactions play a key role in many life processes such as molecular recognition, molecular binding, signal transmission, and cell metabolism. Examples of interaction forces include hydrogen bonding, hydrophobic...
4.
A Comparative Assessment of Predictive Accuracies of Conventional and Machine Learning Scoring Functions for Protein-Ligand Binding Affinity Prediction
Ashtawy H, Mahapatra N
IEEE/ACM Trans Comput Biol Bioinform . 2015 Sep; 12(2):335-47. PMID: 26357221
Accurately predicting the binding affinities of large diverse sets of protein-ligand complexes efficiently is a key challenge in computational biomolecular science, with applications in drug discovery, chemical biology, and structural...
5.
Machine-learning scoring functions for identifying native poses of ligands docked to known and novel proteins
Ashtawy H, Mahapatra N
BMC Bioinformatics . 2015 Apr; 16 Suppl 6:S3. PMID: 25916860
Background: Molecular docking is a widely-employed method in structure-based drug design. An essential component of molecular docking programs is a scoring function (SF) that can be used to identify the...
6.
BgN-Score and BsN-Score: bagging and boosting based ensemble neural networks scoring functions for accurate binding affinity prediction of protein-ligand complexes
Ashtawy H, Mahapatra N
BMC Bioinformatics . 2015 Mar; 16 Suppl 4:S8. PMID: 25734685
Background: Accurately predicting the binding affinities of large sets of protein-ligand complexes is a key challenge in computational biomolecular science, with applications in drug discovery, chemical biology, and structural biology....
7.
A comparative assessment of ranking accuracies of conventional and machine-learning-based scoring functions for protein-ligand binding affinity prediction
Ashtawy H, Mahapatra N
IEEE/ACM Trans Comput Biol Bioinform . 2012 Mar; 9(5):1301-13. PMID: 22411892
Accurately predicting the binding affinities of large sets of protein-ligand complexes efficiently is a key challenge in computational biomolecular science, with applications in drug discovery, chemical biology, and structural biology....