Author

Justin Jose

ORCID 0000-0002-7352-840XUnclaimed ORCID profile · follow to get notified of new preprints

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Justin Jose1 paper

Biology Disciplines

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Bioinformatics· 3

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Approach:deep learning· 3 Assay:protein structure· 3 Research Focus:drug response / drug discovery· 2 Research Focus:protein structure prediction· 2 Approach:statistical model· 2 Approach:graph neural network· 1

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Nidhi JatanaiD· 3 shared Pooja AroraiD· 3 shared Ujjaini Alam AiD· 3 shared Divye SinghiD· 2 shared Kritika Gupta· 1 shared

Activity by Year

2021-2023 · peak 1/yr

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5 top collaborators · 9 mutual co-author links

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3 shared papersBioinformatics

Nidhi JatanaORCID

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3 papers

Recent·Most cited

PandoraRLO: DQN and Graph convolution based method for optimized ligand pose

Justin Jose, Ujjaini Alam A, Divye Singh, Nidhi Jatana, Pooja Arora

PandoraRLO uses reinforcement learning to predict optimized ligand poses for protein-ligand interactions, achieving accuracy within 0.5 to 4 angstroms for many test complexes.

bioRxiv · Mar 12, 2023 · PreprintBioinformatics

PandoraRL: DQN and Graph Convolution based ligand pose learning for SARS-COV1 Mprotease

Justin Jose, Ujjaini Alam A, Pooja Arora, Divye Singh, Nidhi Jatana

This paper introduces PandoraRL, a reinforcement learning model using DQN and graph convolution to predict optimal ligand poses for SARS-CoV1 Mprotease.

IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · Dec 6, 2022Peer-reviewedBioinformatics

Reinforcement Learning Based Approach for Ligand Pose Prediction

Justin Jose, Kritika Gupta, Ujjaini Alam A, Nidhi Jatana, Pooja Arora

This study introduces a reinforcement learning method for predicting optimized ligand poses and identifying binding sites using graph CNNs.

bioRxiv · Apr 10, 2021 · PreprintBioinformatics