Author

M Murshida Mahbub

East West UniversityORCID 0009-0006-5278-4515Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:drug response / drug discovery· 2 Approach:statistical model· 2 Organism:human· 1 Approach:machine learning (classical)· 1 Assay:protein sequence· 1 Assay:proteomics· 1 Research Focus:sequence-to-function modeling· 1

Frequent Co-Authors

Soharth HasnatiD· 3 shared Sal SabilaiD· 2 shared Soaibur RahmaniD· 2 shared ADA Shahinuzzaman· 1 shared Animesh Chowdhury· 1 shared Farha Mohi Suin· 1 shared Fariah Anjum Shifa· 1 shared Farzana YeasminiD· 1 shared

Activity by Year

2024-2024 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 37 mutual co-author links

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3 shared papersBioinformatics

Soharth HasnatORCID

Mutual co-authors in cluster

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3 papers

Recent·Most cited

BioinformaticsbioRxivPreprintNov 18, 2024

High-Throughput Screening Reveals Potential Inhibitors Targeting Trimethoprim-Resistant DfrA1 Protein in Klebsiella pneumoniae and Escherichia coli

Soharth Hasnat, Soaibur Rahman, Meherun Binta Alam…·Tofazzal Islam

This study identifies six potential inhibitors of the DfrA1 protein through high-throughput screening of 3,601 compounds, with DC4 and DC6 showing superior efficacy compared to the control drug.

1 citation

humanstatistical model

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BioinformaticsbioRxivPreprintNov 7, 2024

In silico Identification of Novel Common Drug Targets Against Four Infectious Acinetobacter Species

Soaibur Rahman, Soharth Hasnat, M Murshida Mahbub…·Sal Sabila

This study identifies novel common drug targets against four Acinetobacter species using cluster-based subtractive genomics and druggability analysis.

2 citations

proteomicsstatistical model

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BioinformaticsbioRxivPreprintOct 20, 2024

A Framework for Accurate Prediction of Plastic-Degrading Enzymes using Convolutional Neural Networks

Soharth Hasnat, Fariah Anjum Shifa, Shabab Murshed…·M Murshida Mahbub

This paper develops a CNN-based framework to predict plastic-degrading enzymes from environmental sequences, achieving high accuracy and F1 score.

2 citations

protein sequencemachine learning (classical)

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