Evidence map›Paper›PMID 39508322›Full record

ArticleJournal of chemical theory and computation2024

Advances and Challenges in Milestoning Simulations for Drug-Target Kinetics.

Anupam Anand Ojha, Lane W Votapka, Rommie E Amaro

Abstract read
In one paragraph

Article in Journal of chemical theory and computation, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. Article
  5. ModBind, a Rapid Simulation-Based Predictor of Ligand Binding and Off-Rates.Journal of chemical information and modeling · 2025
    Article
  6. Article
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

3 authors.

Anupam Anand OjhaDepartment of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, United States.ORCID 0000-0001-6588-3092
Lane W VotapkaDepartment of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, United States.ORCID 0000-0002-0865-5867
Rommie E AmaroDepartment of Molecular Biology, University of California San Diego, La Jolla, California 92093, United States.ORCID 0000-0002-9275-9553

Funding

A MULTISCALE APPROACH TO TARGET THE ACHILLES HEEL OF P53 CANCER MUTANTS (Equipment Supplement)R01GM132826 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI AMARO, ROMMIE E · 2019 to 2022
$1.3M
NIGMS NIH HHS R01 GM132826
6 · The paper itself

Abstract

Molecular dynamics simulations have become indispensable for exploring complex biological processes, yet their limitations in capturing rare events hinder our understanding of drug-target kinetics. In this Perspective, we investigate the domain of milestoning simulations to understand this challenge. The milestoning approach divides the phase space of the drug-target complex into discrete cells, offering extended time scale insights. This Perspective traces the history, applications, and future potential of milestoning simulations in the context of drug-target kinetics. It explores the fundamental principles of milestoning, highlighting the importance of probabilistic transitions and transition time independence. Markovian milestoning with Voronoi tessellations is revisited to address the traditional milestoning challenges. While observing the advancements in this field, this Perspective also addresses impending challenges in estimating drug-target unbinding rate constants through milestoning simulations, paving the way for more effective drug design strategies.

Indexed as

Molecular Dynamics SimulationDrug DesignHumansKineticsMarkov Chains

Identifiers

PMID39508322
PMCPMC11603602

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.