Evidence map›Paper›PMID 42423901›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2026

Open-Sourced In Silico Drug Screening.

Audrey G Fikes, Melissa C Srougi

Abstract read
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In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Audrey G FikesChemistry of Life Program, NC State University, Raleigh, NC, USA. agfikes@ncsu.edu.ORCID https://orcid.org/0000-0003-4034-6260
Melissa C SrougiBiotechnology Program, NC State University, Raleigh, NC, USA. mcsrougi@ncsu.edu.ORCID https://orcid.org/0000-0003-2183-0233

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Advances in structure-based computer-aided drug design have yielded robust computational techniques now pivotal for chemical biologists. These tools predict small molecule-protein interactions, significantly accelerating the drug discovery process. Unlike other computational techniques, structure-based design requires prior knowledge of a target molecule's three-dimensional structure, often leveraging molecular docking to predict target-ligand interactions. The primary goal is to design or identify small molecules that can bind to the target with high affinity and modulate its activity. Once a target's binding site(s) are determined, high-throughput in silico screens of large chemical libraries can predict binding energies, non-covalent interactions, and intermolecular forces crucial for target-ligand binding. These powerful methods accelerate the bench-to-bedside process, aiding in the repurposing of FDA-approved drugs, identifying novel ligands, and informing rational drug design, optimization, and/or elucidation of mechanism(s) of action. In this chapter, we describe a structure-based computational approach to perform high-throughput ligand screens of chemical libraries using open-source software programs. We illustrate this workflow with the enzymatic molecular target NAD(P)H:quinone oxidoreductase1 (NQO1), which is overexpressed in a number of human solid tumors. Importantly, this adaptable workflow requires minimal coding knowledge.

Indexed as

Drug DiscoveryBinding SitesComputer SimulationDrug DesignDrug Evaluation, PreclinicalHumansLigandsMolecular Docking SimulationNAD(P)H Dehydrogenase (Quinone)Small Molecule LibrariesSoftwareLigandsNAD(P)H Dehydrogenase (Quinone)NQO1 protein, humanSmall Molecule LibrariesDrug discoveryDrug screeningIn silicoMolecular dockingOpen-sourceStructure–activity relationship modeling

Identifiers

PMID42423901

What OpenQuestion holds

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Registered trials

None linked

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.