What question did this study set out to answer?

To review computational and artificial intelligence-assisted methodologies that accelerate vaccine candidate discovery against multi-drug resistant bacterial pathogens.

October 9, 2023Frontiers in BacteriologyOpen Access

Artificial intelligence in accelerating vaccine development - current and future perspectives

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Authors

RKRahul Kaushik RKRavi Kant MCMyron Christodoulides

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Overview

Narrative review demonstrates computational artificial intelligence methods for identifying bacterial vaccine candidates, highlighting novel pipelines to counter antimicrobial resistance.

Key Points

To review computational and artificial intelligence-assisted methodologies that accelerate vaccine candidate discovery against multi-drug resistant bacterial pathogens.
Synthesized literature on computational pipelines that replace conventional empirical discovery workflows.
Evaluated artificial intelligence algorithms used to screen and identify antigenic targets in multi-drug resistant bacteria.
Identified computational approaches as effective alternatives that compress the timeline of traditional vaccine discovery.
Highlighted algorithmic screening strategies that successfully detect viable vaccine candidates against resistant bacterial targets.

Cite This Study

Kaushik et al. (2023) studied this question.

synapsesocial.com/papers/69dbe01a387cf70698689172 https://doi.org/10.3389/fbrio.2023.1258159

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October 9, 2023Frontiers in BacteriologyOpen Access

Artificial intelligence in accelerating vaccine development - current and future perspectives

View Full Paper

Ask AI

Bookmark

Authors

RKRahul Kaushik RKRavi Kant MCMyron Christodoulides

Discussion

Member takes

Overview

Narrative review demonstrates computational artificial intelligence methods for identifying bacterial vaccine candidates, highlighting novel pipelines to counter antimicrobial resistance.

Key Points

To review computational and artificial intelligence-assisted methodologies that accelerate vaccine candidate discovery against multi-drug resistant bacterial pathogens.
Synthesized literature on computational pipelines that replace conventional empirical discovery workflows.
Evaluated artificial intelligence algorithms used to screen and identify antigenic targets in multi-drug resistant bacteria.
Identified computational approaches as effective alternatives that compress the timeline of traditional vaccine discovery.
Highlighted algorithmic screening strategies that successfully detect viable vaccine candidates against resistant bacterial targets.

Cite This Study

Kaushik et al. (2023) studied this question.

synapsesocial.com/papers/69dbe01a387cf70698689172 https://doi.org/10.3389/fbrio.2023.1258159

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