ArticleMethods in molecular biology (Clifton, N.J.)2025
A Python-Based Workflow for Computing and Extracting Drug-Like Compounds from PubChem.
Article in Methods in molecular biology (Clifton, N.J.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drugs play a crucial role in treating numerous diseases. However, not all compounds have the properties required to function effectively within biological systems. Lipinski's rule of five, a widely accepted guideline, helps assess drug-likeness by screening for molecular properties associated with bioavailability. To enhance the efficiency of computational drug discovery, especially for docking studies with viral targets, filters are applied to the vast array of compounds in public databases like PubChem. By excluding compounds unlikely to be effective as therapeutic agents, researchers can focus on those with higher potential, streamlining subsequent, computationally intensive analyses. This chapter introduces a python-based workflow for filtering PubChem Compounds, prioritizing those that meet Lipinski's criteria and are more likely to demonstrate biological efficacy. This approach can be used as an example for targeted and efficient drug development efforts.
Indexed as
Identifiers
40455164What OpenQuestion holds
Registered trials
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.