Evidence map›Paper›PMID 38095857›Full record

ArticleBriefings in bioinformatics2023

Differentiable rotamer sampling with molecular force fields.

Congzhou M Sha, Jian Wang, Nikolay V Dokholyan

Abstract read
In one paragraph

Article in Briefings in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Peptide-based drug design using generative AI.Chemical communications (Cambridge, England) · 2026
    Review
  2. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Congzhou M ShaDepartment of Engineering Science and Mechanics, Penn State University, University Park, PA USA.ORCID 0000-0001-5301-9459
Jian WangDepartment of Pharmacology, Penn State College of Medicine, Hershey, PA USA.ORCID 0000-0001-7768-2802
Nikolay V DokholyanDepartment of Engineering Science and Mechanics, Penn State University, University Park, PA USA.ORCID 0000-0002-8225-4025

Funding

Nanoscale programming of cellular and physiological phenotypes: EquipmentR35GM134864 · NIGMS · UNIVERSITY OF VIRGINIA · PI Nikolay Dokholyan · 2020 to 2026
$5.2M
AI-based Mapping of Complex Cannabis Extracts in Pain PathwaysR01AT012053 · NCCIH · PENNSYLVANIA STATE UNIV HERSHEY MED CTR · PI Nikolay Dokholyan, KENT E VRANA · 2023 to 2026
$2.4M
NCCIH NIH HHS R01 AT012053NIGMS NIH HHS R35 GM134864NIH HHS 1R35 GM134864
6 · The paper itself

Abstract

Molecular dynamics (MD) is the primary computational method by which modern structural biology explores macromolecule structure and function. Boltzmann generators have been proposed as an alternative to MD, by replacing the integration of molecular systems over time with the training of generative neural networks. This neural network approach to MD enables convergence to thermodynamic equilibrium faster than traditional MD; however, critical gaps in the theory and computational feasibility of Boltzmann generators significantly reduce their usability. Here, we develop a mathematical foundation to overcome these barriers; we demonstrate that the Boltzmann generator approach is sufficiently rapid to replace traditional MD for complex macromolecules, such as proteins in specific applications, and we provide a comprehensive toolkit for the exploration of molecular energy landscapes with neural networks.

Indexed as

Molecular Dynamics SimulationProteinsNeural Networks, ComputerThermodynamicsProteinsBoltzmann generatordifferentiable programmingmolecular dynamicsneural networkrotameric samplingstatistical mechanics

Identifiers

PMID38095857
PMCPMC10720392

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.