AI-driven parametrization of Michaelis-Menten maximal velocity: Advancing in silico new approach methodologies (NAMs).

Achilleas Karakoltzidis, Spyros P Karakitsios, Dimosthenis Α Sarigiannis

Abstract read

In one paragraph

Article in NAM journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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1 · What the graph read from it

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Achilleas KarakoltzidisAristotle University of Thessaloniki, Department of Chemical Engineering, Environmental Engineering Laboratory, University Campus, Thessaloniki 54124, Greece.

Spyros P KarakitsiosAristotle University of Thessaloniki, Department of Chemical Engineering, Environmental Engineering Laboratory, University Campus, Thessaloniki 54124, Greece.

Dimosthenis Α SarigiannisAristotle University of Thessaloniki, Department of Chemical Engineering, Environmental Engineering Laboratory, University Campus, Thessaloniki 54124, Greece.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The development of mechanistic systems biology models necessitates the utilization of numerous kinetic parameters once the enzymatic mode of action has been identified. Simultaneously, wet lab experimentation is associated with particularly high costs, does not adhere to principles of reducing the number of animal tests, and is a time-consuming procedure. Alternatively, an artificial intelligence-based method is proposed that utilizes enzyme amino acid structures as input data. This method combines NLP techniques with molecular fingerprints of the catalysed reaction to determine Michaelis-Menten maximal velocities (

Indexed as

Deep learningEnzyme structureMaximal velocityNAMsQSPRVmax

Identifiers

PMID42369428

DOI10.1016/j.namjnl.2025.100012

PMCPMC13289207

What Socratic holds

Textmetadata

LicenceCC BY

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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 Socratic graph.