Nature Search

Disruptive Strategies for Removing Drug Discovery Bottlenecks

Sean Ekins
Chris Waller
Antony Williams
ResearchOpen Access05 Mar 2012
Nature Precedings

P: 1
The Goldilocks paradigm: comparing classical machine learning, large language models, and few-shot learning for drug discovery applications

Machine learning (ML) is a powerful tool in the field of drug discovery, with the continuous development of new models, however, rational selection of the most appropriate model based on the task remains challenging. Here, the authors explore the capabilities of classical ML algorithms and newer models over a range of dataset tasks and show an optimal zone for each model type, developing a predictive model to aid in the selection of a modeling method based on dataset size and diversity.

Scott H. Snyder
Patricia A. Vignaux
Sean Ekins
ResearchOpen Access12 Jun 2024
Communications Chemistry

Volume: 7, P: 1-11
Industrializing rare disease therapy discovery and development

Sean Ekins
Correspondence08 Feb 2017
Nature Biotechnology

Volume: 35, P: 117-118
A teachable moment for dual-use

Fabio Urbina
Filippa Lentzos
Sean Ekins
Correspondence12 Jul 2022
Nature Machine Intelligence

Volume: 4, P: 607
Dual use of artificial-intelligence-powered drug discovery

An international security conference explored how artificial intelligence (AI) technologies for drug discovery could be misused for de novo design of biochemical weapons. A thought experiment evolved into a computational proof.

Fabio Urbina
Filippa Lentzos
Sean Ekins
Comments & Opinion07 Mar 2022
Nature Machine Intelligence

Volume: 4, P: 189-191
Non-classical transpeptidases yield insight into new antibacterials

Carbapenem β-lactam antibiotics target non-classical transpeptidases, the L,D-transpeptidases, which act in an alternative Mycobacterium tuberculosis peptidoglycan synthesis pathway, informing the design of evolved carbapenems with improved antibacterial activity.

Pankaj Kumar
Amit Kaushik
Gyanu Lamichhane
Research07 Nov 2016
Nature Chemical Biology

Volume: 13, P: 54-61
Opportunities and challenges using artificial intelligence in ADME/Tox

At the recent Artificial Intelligence Applications in Biopharma Summit in Boston, USA, a panel of scientists from industry who work at the interface of machine learning and pharma discussed the diverging opinions on the past, present and future role of AI for ADME/Tox in drug discovery and development.

Barun Bhhatarai
W. Patrick Walters
Sean Ekins
Comments & Opinion18 Apr 2019
Nature Materials

Volume: 18, P: 418-422
Exploiting machine learning for end-to-end drug discovery and development

This Perspective describes the application of machine learning models in the design, synthesis and characterisation of molecules at different stages in the drug discovery and development process.

Sean Ekins
Ana C. Puhl
Alex M. Clark
Reviews18 Apr 2019
Nature Materials

Volume: 18, P: 435-441
Synergistic drug combinations and machine learning for drug repurposing in chordoma

Edward Anderson
Tammy M. Havener
Sean Ekins
ResearchOpen Access31 Jul 2020
Scientific Reports

Volume: 10, P: 1-10
Cheminformatics Analysis and Modeling with MacrolactoneDB

Phyo Phyo Kyaw Zin
Gavin J. Williams
Sean Ekins
ResearchOpen Access14 Apr 2020
Scientific Reports

Volume: 10, P: 1-12
A diarylamine derived from anthranilic acid inhibits ZIKV replication

Suely Silva
Jacqueline Farinha Shimizu
Ana Carolina Gomes Jardim
ResearchOpen Access27 Nov 2019
Scientific Reports

Volume: 9, P: 1-12
A multitarget approach to drug discovery inhibiting Mycobacterium tuberculosis PyrG and PanK

Laurent R. Chiarelli
Giorgia Mori
Maria Rosalia Pasca
ResearchOpen Access16 Feb 2018
Scientific Reports

Volume: 8, P: 1-10
Predictive modeling targets thymidylate synthase ThyX in Mycobacterium tuberculosis

Kamel Djaout
Vinayak Singh
Sean Ekins
ResearchOpen Access10 Jun 2016
Scientific Reports

Volume: 6, P: 1-11

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Disruptive Strategies for Removing Drug Discovery Bottlenecks

The Goldilocks paradigm: comparing classical machine learning, large language models, and few-shot learning for drug discovery applications

Industrializing rare disease therapy discovery and development

A teachable moment for dual-use

Dual use of artificial-intelligence-powered drug discovery

Non-classical transpeptidases yield insight into new antibacterials

Opportunities and challenges using artificial intelligence in ADME/Tox

Exploiting machine learning for end-to-end drug discovery and development

Synergistic drug combinations and machine learning for drug repurposing in chordoma

Cheminformatics Analysis and Modeling with MacrolactoneDB

A diarylamine derived from anthranilic acid inhibits ZIKV replication

A multitarget approach to drug discovery inhibiting Mycobacterium tuberculosis PyrG and PanK

Predictive modeling targets thymidylate synthase ThyX in Mycobacterium tuberculosis

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