ArticleChemistryOpen2019
Synthetic Activators of Cell Migration Designed by Constructive Machine Learning.
Article in ChemistryOpen, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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.
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.
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Who cites it
7 citing papers in PubMed, 14 citations in OpenAlex.
- Effective Ligand Design: Zinc Complexes with Guanidine Hydroquinoline Ligands for Fast Lactide Polymerization and Chemical Recycling.ChemSusChem · 2022Article
- Identification of novel off targets of baricitinib and tofacitinib by machine learning with a focus on thrombosis and viral infection.Scientific reports · 2022Article
- Combining generative artificial intelligence and on-chip synthesis for de novo drug design.Science advances · 2021Article
- Virtual Screening and Design with Machine Intelligence Applied to Pim-1 Kinase Inhibitors.Molecular informatics · 2020Article
- Rethinking drug design in the artificial intelligence era.Nature reviews. Drug discovery · 2020Review
- Synthetic Activators of Cell Migration Designed by Constructive Machine Learning.ChemistryOpen · 2019Article
- Baricitinib and tofacitinib off-target profile, with a focus on Alzheimer's disease.Alzheimer's & dementia (New York, N. Y.)Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Constructive machine learning aims to create examples from its learned domain which are likely to exhibit similar properties. Here, a recurrent neural network was trained with the chemical structures of known cell-migration modulators. This machine learning model was used to generate new molecules that mimic the training compounds. Two top-scoring designs were synthesized, and tested for functional activity in a phenotypic spheroid cell migration assay. These computationally generated small molecules significantly increased the migration of medulloblastoma cells. The results further corroborate the applicability of constructive machine learning to the de novo design of druglike molecules with desired properties.
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Registered trials
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.