Machine learning models to accelerate the design of polymeric long-acting injectables.

Pauric Bannigan, Zeqing Bao, Riley J Hickman, Matteo Aldeghi, Florian Häse, Alán Aspuru-Guzik, Christine Allen

January 2023 Nat Commun

Synopsis of Social media discussions

Several posts describe the article’s topic as a promising application of machine learning in drug development, using words like 'accelerate' and 'design' to suggest innovation. The tone is generally positive and curious, but many posts are retweets or brief mentions, which indicates moderate interest without deep exploration. This pattern reflects recognition of the research’s importance but limited detailed engagement.

A
Agreement
 Moderate agreement

The posts generally acknowledge the significance of applying machine learning to improve drug formulation, with some expressing enthusiasm and support for the research.

I
Interest
 High level of interest

Many discussions highlight curiosity or recognition of the topic's relevance, emphasizing how machine learning could revolutionize pharmaceutical design.

E
Engagement
 Moderate level of engagement

Despite frequent mentions, the posts tend to be retweets or brief acknowledgments, indicating moderate engagement rather than deep analysis.

I
Impact
 Moderate level of impact

While some discussions note potential benefits, most posts suggest the research could have meaningful influence without explicitly demonstrating it.

Social Mentions

YouTube

1 Videos

Twitter

28 Posts

Blogs

6 Articles

News

18 Articles

Metrics

Video Views

38

Total Likes

34

Extended Reach

123,613

Social Features

53

Timeline: Posts about article

Top Social Media Posts

Posts referencing the article

Machine Learning for Designing Polymeric Long-Acting Injectables

Machine Learning for Designing Polymeric Long-Acting Injectables

Long-acting injectables are vital for chronic disease treatment, enhancing efficacy and patient adherence. This study demonstrates how machine learning predicts drug release and supports formulation design, potentially reducing development time and costs.

STEM RTCL TV

April 28, 2023

38 views

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    January 21, 2023

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    January 13, 2023

  • Dr. Ajay Gupta
    @Ajaykgupta15 (Twitter)

    Machine learning models to accelerate the design of polymeric long-acting injectables: The use of polymer materials in a drug formulation strategy can offer unparalleled diversity owing to the ability to synthesize materials with a wide range of propertie…https://t.co/0bfa3eWz20
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  • Dr. Christine Allen
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    Delighted to share our manuscript on "Machine Learning Models to Accelerate the Design of Polymeric Long-Acting Injectables" now posted on @ChemRxiv. Please read and share feedback. Collaboration with @A_Aspuru_Guzik and made possible by @acceleration_c. https://t.co/H3OqXGacGW https://t.co/XARj1xXVst
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Abstract Synopsis

  • *Using polymer materials in these formulations allows for diverse drug properties, but predicting their performance is complex due to various interacting factors.
  • *The study shows that machine learning can predict drug release from these systems and aid in designing new formulations, potentially reducing development time and costs.