Evidence map›Paper›PMID 33006893›Full record

ReviewChemical reviews2020

Thermodynamics and Kinetics of Drug-Target Binding by Molecular Simulation.

Sergio Decherchi, Andrea Cavalli

Open access · hybridAbstract readReview
In one paragraph

Review in Chemical reviews, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 123 papers.

0numbers the graph read from it
0cells of the map it votes in
123citing papers in PubMed
11.3field-weighted citation impact, top 1% of its field
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

123 citing papers in PubMed, 266 citations in OpenAlex.

  1. Article
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  5. Phytochemical Profiling and Computational Screening ofPharmaceuticals (Basel, Switzerland) · 2026
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  11. Comparative insights into single stranded RNAs interaction with methylene blue and new methylene blue: a photophysical, thermodynamic and modeling study.Photochemical & photobiological sciences : Official journal of the European Photochemistry Association and the European Society for Photobiology · 2026
    Article
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63 more citing papers are in PubMed but not listed here.

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 at 1 institution in 1 country.

Sergio DecherchiComputational and Chemical Biology, Fondazione Istituto Italiano di Tecnologia, 16163 Genoa, Italy.ORCID 0000-0001-8371-2270
Andrea CavalliComputational and Chemical Biology, Fondazione Istituto Italiano di Tecnologia, 16163 Genoa, Italy.ORCID 0000-0002-6370-1176
Italian Institute of Technology · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Computational studies play an increasingly important role in chemistry and biophysics, mainly thanks to improvements in hardware and algorithms. In drug discovery and development, computational studies can reduce the costs and risks of bringing a new medicine to market. Computational simulations are mainly used to optimize promising new compounds by estimating their binding affinity to proteins. This is challenging due to the complexity of the simulated system. To assess the present and future value of simulation for drug discovery, we review key applications of advanced methods for sampling complex free-energy landscapes at near nonergodicity conditions and for estimating the rate coefficients of very slow processes of pharmacological interest. We outline the statistical mechanics and computational background behind this research, including methods such as steered molecular dynamics and metadynamics. We review recent applications to pharmacology and drug discovery and discuss possible guidelines for the practitioner. Recent trends in machine learning are also briefly discussed. Thanks to the rapid development of methods for characterizing and quantifying rare events, simulation's role in drug discovery is likely to expand, making it a valuable complement to experimental and clinical approaches.

Indexed as

Molecular Dynamics SimulationThermodynamicsDrug DiscoveryKineticsPharmaceutical PreparationsPharmaceutical Preparations

Identifiers

PMID33006893
PMCPMC8011912
OpenAlexW3090513030

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.