Automatic molecular fragmentation by evolutionary optimisation.

Fiona C Y Yu, Jorge L Gálvez Vallejo, Giuseppe M J Barca

Abstract read

In one paragraph

Article in Journal of cheminformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing 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

1 citing paper in PubMed.

Fragme∩t: An Open-Source Framework for Multiscale Quantum Chemistry Based on Fragmentation.Wiley interdisciplinary reviews. Computational molecular science
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.

Fiona C Y YuSchool of Computing, Australian National University, Canberra, 2601, ACT, Australia.

Jorge L Gálvez VallejoSchool of Computing, Australian National University, Canberra, 2601, ACT, Australia.

Giuseppe M J BarcaSchool of Computing and Information Technology, The University of Melbourne, Melbourne, 3052, VIC, Australia. giuseppe.barca@unimelb.edu.au.

Funding

Commonwealth Scientific and Industrial Research Organisation Pawsey Centre for Extreme Scale Readiness

6 · The paper itself

Abstract

Molecular fragmentation is an effective suite of approaches to reduce the formal computational complexity of quantum chemistry calculations while enhancing their algorithmic parallelisability. However, the practical applicability of fragmentation techniques remains hindered by a dearth of automation and effective metrics to assess the quality of a fragmentation scheme. In this article, we present the Quick Fragmentation via Automated Genetic Search (QFRAGS), a novel automated fragmentation algorithm that uses a genetic optimisation procedure to generate molecular fragments that yield low energy errors when adopted in Many Body Expansions (MBEs). Benchmark testing of QFRAGS on protein systems with less than 500 atoms, using two-body (MBE2) and three-body (MBE3) MBE calculations at the HF/6-31G* level, reveals mean absolute energy errors (MAEE) of 20.6 and 2.2 kJ

Indexed as

Fragment molecular orbitalMany body expansionMolecular fragmentationMolecular graph theoryQuantum chemical calculations

Identifiers

PMID39160576

DOI10.1186/s13321-024-00896-z

PMCPMC11331744

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.