Evidence map›Paper›PMID 42604907›Full record

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

Quantum-Driven High-Throughput Docking: Advances in Molecular Scoring and Drug Discovery.

Nicolás Arrupe, Claudio N Cavasotto

Abstract readReview
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In one paragraph

Review 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.

Nicolás ArrupeComputational Drug Design and Biomedical Informatics Laboratory, Instituto de Investigaciones en Medicina Traslacional (IIMT), Universidad Austral-CONICET, Pilar, Buenos Aires, Argentina.
Claudio N CavasottoComputational Drug Design and Biomedical Informatics Laboratory, Instituto de Investigaciones en Medicina Traslacional (IIMT), Universidad Austral-CONICET, Pilar, Buenos Aires, Argentina. ccavasotto@austral.edu.ar.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Computational methods are consolidated tools in early drug lead discovery. Among them, high-throughput molecular docking (HTD) is widely used to identify bioactive compounds within large chemical libraries by assessing likely binding modes and assigning docking scores. These methods also inform how hits may be modified to improve protein-ligand interactions, thus guiding lead optimization while reducing time and cost compared with experimental screening. For more than a decade, advances in methodology and computing power have renewed interest in applying quantum mechanics (QM) to macromolecular systems. A QM description of molecular association can provide improved accuracy over classical molecular mechanics (MM)-methods, since phenomena such as covalent bond formation, electronic polarization, charge transfer, bond rearrangement, and halogen bonding require explicit treatment of electrons. Here, we review recent developments in QM-based molecular docking and HTD, focusing on cases where QM is used explicitly in docking workflows.

Indexed as

Drug DiscoveryMolecular Docking SimulationProteinsHigh-Throughput Screening AssaysLigandsProtein BindingQuantum MechanicsQuantum TheoryLigandsProteinsDrug discoveryHigh-throughput dockingMolecular dockingQuantum mechanicsSemiempirical methods

Identifiers

What OpenQuestion holds

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Registered trials

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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.