A Review on Applications of Artificial Intelligence (AI) in Monoclonal Antibody (mAb) Manufacturing.

Abstract readReview

In one paragraph

Review in Molecules (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Fawad AbidiInstitute for Materials and Processes (IMP), School of Engineering, University of Edinburgh, Edinburgh EH9 3FB, UK.

Dimitrios I GerogiorgisInstitute for Materials and Processes (IMP), School of Engineering, University of Edinburgh, Edinburgh EH9 3FB, UK.ORCID 0000-0002-2210-6784

Funding

Engineering and Physical Sciences Research Council EP/V028618/1Higher Education Commission (HEC) of Pakistan PhD FellowshipRoyal Society Royal Society International Exchanges Programme grant (IES\R2\232014)Royal Society Royal Society Short Industrial Fellowship

6 · The paper itself

Abstract

This review paper offers a comprehensive exploration of the many Artificial Intelligence (AI) applications in monoclonal antibody (mAb) manufacturing, covering a wide spectrum of topics, including upstream and downstream processes, process control, optimisation, product property monitoring and regulatory compliance. We present several case studies showcasing the predictive modelling capabilities of AI and ML algorithms for cell culture optimisation, media formulation, and bioreactor control, highlighting their potential to enhance cell growth, productivity, and product quality. Furthermore, we explore its potential in process monitoring, fault detection, and real-time decision-making, towards improved process robustness and reduced production costs.

Indexed as

Antibodies, MonoclonalArtificial IntelligenceAlgorithmsAnimalsBioreactorsHumansAntibodies, Monoclonalartificial intelligencedeep learningmAbsmachine learningneural networks

Identifiers

PMID42796599

DOI10.3390/molecules31183313

PMCPMC13609787

What OpenQuestion holds

Textmetadata

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.