Evidence map›Paper›PMID 42705229›Full record

ArticleSTAR protocols2026

An extension of jamdock-suite for virtual screening from raw chemical datasets.

Shahariar Emon, Md Mozzammel Haque, Iftekhar Alam, Md Anwarul Haque, Jahangir Md Alam

Abstract read
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Article in STAR protocols, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Shahariar EmonDepartment of Biotechnology and Genetic Engineering, Islamic University, Kushtia 7003, Bangladesh.
Md Mozzammel HaqueDepartment of Urology, Satkhira Medical College, Satkhira 9400, Bangladesh.
Iftekhar AlamPlant Biotechnology Division, National Institute of Biotechnology, Ganakbari, Ashulia, Savar, Dhaka 1349, Bangladesh.
Md Anwarul HaqueDepartment of Biotechnology and Genetic Engineering, Islamic University, Kushtia 7003, Bangladesh.
Jahangir Md AlamDepartment of Biotechnology and Genetic Engineering, Islamic University, Kushtia 7003, Bangladesh. Electronic address: jahangir@btge.iu.ac.bd.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Virtual screening of chemical libraries has long been a cornerstone for identifying candidate bioactive compounds. Recently, many tools have been developed to make virtual screening more accessible. Among them, the jamdock-suite provides an automated and user-friendly workflow that encompasses the entire process from library generation to docking evaluation. However, its library generation is restricted to the ZINC database and cannot process user-supplied chemical datasets. Here, we present semdock, an extension of the original workflow that enables the preparation of docking-ready ligands from raw SDF, SMILES, and CSV datasets through an automated ligand preparation procedure. The workflow was validated using 3,230 FDA-approved compounds and multiple protein targets. We also implemented ligand-wise CPU-parallel docking, reducing runtime by approximately 30% compared with conventional multithreaded execution. Evaluation across multiple protein targets confirmed that the extended workflow preserves the utility of the original framework while expanding its use to diverse public and user-defined datasets.

Indexed as

BioinformaticsBiophysicsHigh Throughput Screening

Identifiers

PMID42705229
PMCPMC13615501

What OpenQuestion holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.