Mol Pharm November 2022 PMID 36269563

Machine Learning Models Identify New Inhibitors for Human OATP1B1.

Thomas R Lane, Fabio Urbina, Xiaohong Zhang, Margret Fye, Jacob Gerlach, Stephen H Wright, Sean Ekins

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Mentions

Peak: 5 in Oct 2022

across 2 sources since Oct 2022

Extended reach

29,651

potential audience

Total likes

Video views

105

Mentions per month

Oct 2022 – Dec 2025

Where it's discussed

X / Twitter7 posts
YouTube2 videos

AI synopsis

What people are saying

The discussions clearly express enthusiasm for the novel application of machine learning in pharmacology, as seen in posts emphasizing keywords like 'identify,' 'predict,' and 'new inhibitors,' which reflect excitement about advancements that could improve drug safety and efficiency. The tone is optimistic, with some posts referencing the potential for future digital drug development, indicating recognition of its moderate yet promising impact.

AgreementModerate agreement

Most discussions recognize the significance of using machine learning to identify drug inhibitors, indicating general support for the research.

InterestHigh interest

The social discussions show high interest, with multiple mentions of AI, machine learning, and their roles, suggesting enthusiasm and curiosity about the topic.

EngagementModerate engagement

Participants engage at a moderate level, repeating the article's title and highlighting its importance, but few delve into detailed analysis.

ImpactModerate impact

The discussions imply that the research could have meaningful implications for drug development and understanding drug interactions, though they stop short of detailed impact analysis.

Abstract in brief

Researchers developed machine learning models to identify new inhibitors of the human transporter OATP1B1, which is involved in drug interactions in the liver.
They tested seven different algorithms using chemical fingerprints and found that Support Vector Classifier (SVC) performed best in predicting inhibitors, both in cross-validation and with new test data.
The models successfully predicted several novel inhibitors, which were confirmed through lab experiments, demonstrating their potential to help assess drug interactions digitally in future drug development.

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All mentions

Machine Learning Identifies New Inhibitors for Human OATP1B1

This video summarizes a study where machine learning models were used to identify new inhibitors of the human transporter OATP1B1, crucial for drug interactions in the liver. The models successfully predicted novel…

Share CHO Cell research papers EN · Dec 1, 2025 57 views

Machine Learning Identifies New Inhibitors for OATP1B1 Transporter

This video summarizes a study where machine learning models, especially Support Vector Classifier, successfully predicted new inhibitors for the human transporter OATP1B1, aiding in digital drug interaction assessments.

Share CHO Cell research papers EN · Oct 18, 2025 48 views

CHO Cell Papers

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Nov 15, 2025 · 63 followers View post →

CHO Cell Papers

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Oct 24, 2025 · 63 followers View post →

AI News Feed

@AINewsFeed · X

Machine Learning Models Identify New Inhibitors for Human OATP1B1 - ACS Publications https://t.co/NJDX2D9a7s #ai #ml #dl

Oct 22, 2022 · 9,379 followers View post →

godfrey™

@godfrey_G_ · X

RT @ML_Chem: Machine Learning Models Identify New Inhibitors for Human OATP1B1 #machinelearning https://t.co/1cAIvteYxb

Oct 22, 2022 · 1 share · 4,999 followers View post →

Machine Learning in Chemistry

@ML_Chem · X

Machine Learning Models Identify New Inhibitors for Human OATP1B1 #machinelearning https://t.co/1cAIvteYxb

Oct 22, 2022 · 3 likes · 1 share · 5,399 followers View post →

AI News Feed

@AINewsFeed · X

Machine Learning Models Identify New Inhibitors for Human OATP1B1 - ACS Publications https://t.co/G6fRWuCowz #ai #ml #dl

Oct 21, 2022 · 9,379 followers View post →

Phi Life Sciences

@PhiLifeSciences · X

[ASAP] Machine Learning Models Identify New Inhibitors for Human OATP1B1 https://t.co/dDcmbiScQd

Oct 21, 2022 · 260 followers View post →