Nihar R. Mahapatra

article

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 Transactions on Computational Biology and Bioinformatics > 2015 > 12 > 2 > 335 - 347

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 biology. Since a scoring function (SF) is used to score, rank, and identify potential drug leads, the fidelity with which it predicts the affinity of a ligand candidate...

article

Machine-learning scoring functions for identifying native poses of ligands docked to known and novel proteins

Hossam M Ashtawy, Nihar R Mahapatra

BMC Bioinformatics > 2015 > 16 > 6 > 1-17

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 most stable binding pose of a ligand, when bound to a receptor protein, from among a large set of candidate poses. Despite intense efforts in developing conventional SFs, which are either force-field...

article

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 Bioinformatics > 2015 > 16 > 4 > 1-12

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. Since a scoring function (SF) is used to score, rank, and identify drug leads, the fidelity with which it predicts the affinity of a ligand candidate for a protein's...

article

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 Transactions on Computational Biology and Bioinformatics > 2012 > 9 > 5 > 1301 - 1313

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. Since a scoring function (SF) is used to score, rank, and identify drug leads, the fidelity with which it predicts the affinity of a ligand candidate for a protein's...

chapter

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

2011 IEEE International Conference on Bioinformatics and Biomedicine > 627 - 630

2011 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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. Since a scoring function (SF) is used to score, rank, and identify drug leads, the fidelity with which it predicts the affinity of a ligand candidate for a protein's binding site...

article

An efficient delay-optimal distributed termination detection algorithm

Nihar R. Mahapatra, Shantanu Dutt

Journal of Parallel and Distributed Computing > 2007 > 67 > 10 > 1047-1066

Distributed termination detection is a fundamental problem in parallel and distributed computing and numerous schemes with different performance characteristics have been proposed. These schemes, while being efficient with regard to one performance metric, prove to be inefficient in terms of other metrics. A significant drawback shared by all previous methods is that, on most popular topologies, they...

article

Adaptive Quality Equalizing: High-performance load balancing for parallel branch-and-bound across applications and computing systems

Nihar R. Mahapatra, Shantanu Dutt

Parallel Computing > 2004 > 30 > 7 > 867-881

In this paper, we present an adaptive version of our previously proposed quality equalizing (QE) load balancing strategy that attempts to maximize the performance of parallel branch-and-bound (B&B) by adapting to application and target computing system characteristics. Adaptive QE (AQE) incorporates the following salient adaptive features: (1) Anticipatory quantitative and qualitative load balancing...

article

Hardware-Efficient and Highly Reconfigurable 4- and 2-Track Fault-Tolerant Designs for Mesh-Connected Arrays

Nihar R. Mahapatra, Shantanu Dutt

Journal of Parallel and Distributed Computing > 2001 > 61 > 10 > 1391-1411

We consider m-track models for constructing fault-tolerant (FT) mesh systems which have one primary and m spare tracks per row and column, switches at the intersection of these tracks, and spare processors at the boundaries. A faulty system is reconfigured by finding for each fault u a reconfiguration path from the fault to a spare in which, starting from the fault u, a processor is replaced or “covered”...

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Search results for: Nihar R. Mahapatra

A Comparative Assessment of Predictive Accuracies of Conventional and Machine Learning Scoring Functions for Protein-Ligand Binding Affinity Prediction

Machine-learning scoring functions for identifying native poses of ligands docked to known and novel proteins

BgN-Score and BsN-Score: Bagging and boosting based ensemble neural networks scoring functions for accurate binding affinity prediction of protein-ligand complexes

A Comparative Assessment of Ranking Accuracies of Conventional and Machine-Learning-Based Scoring Functions for Protein-Ligand Binding Affinity Prediction

A Comparative Assessment of Conventional and Machine-Learning-Based Scoring Functions in Predicting Binding Affinities of Protein-Ligand Complexes

An efficient delay-optimal distributed termination detection algorithm

Adaptive Quality Equalizing: High-performance load balancing for parallel branch-and-bound across applications and computing systems

Hardware-Efficient and Highly Reconfigurable 4- and 2-Track Fault-Tolerant Designs for Mesh-Connected Arrays

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