ArticleJournal of the American Chemical Society2019
Data-Driven Construction of Antitumor Agents with Controlled Polypharmacology.
Article in Journal of the American Chemical Society, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 20 citations in OpenAlex.
- Targeting the RNA-Binding Site of SARS-CoV-2 NSP13 by FRASE-bot in CACHE Challenge #2.Journal of chemical information and modeling · 2026Article
- Exploiting structural variability in the kinase back-pocket to modulate polypharmacology of TAM inhibitors.European journal of medicinal chemistry · 2025Article
- Trends and Pitfalls in the Progress of Network Pharmacology Research on Natural Products.Pharmaceuticals (Basel, Switzerland) · 2025Article
- In silico fragment-based discovery of CIB1-directed anti-tumor agents by FRASE-bot.Nature communications · 2024Article
- Targeting NEDD8-activating enzyme for cancer therapy: developments, clinical trials, challenges and future research directions.Journal of hematology & oncology · 2023Review
- Advances in Computational Polypharmacology.Molecular informatics · 2022Review
- Phenotypic drug discovery: recent successes, lessons learned and new directions.Nature reviews. Drug discovery · 2022Review
- Network Pharmacology of Adaptogens in the Assessment of Their Pleiotropic Therapeutic Activity.Pharmaceuticals (Basel, Switzerland) · 2022Review
- MERTK in cancer therapy: Targeting the receptor tyrosine kinase in tumor cells and the immune system.Pharmacology & therapeutics · 2020Review
- An up-to-date overview of computational polypharmacology in modern drug discovery.Expert opinion on drug discovery · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
20 authors at 2 institutions in 1 country.
Funding
Abstract
Controlling which particular members of a large protein family are targeted by a drug is key to achieving a desired therapeutic response. In this study, we report a rational data-driven strategy for achieving restricted polypharmacology in the design of antitumor agents selectively targeting the TYRO3, AXL, and MERTK (TAM) family tyrosine kinases. Our computational approach, based on the concept of fragments in structural environments (FRASE), distills relevant chemical information from structural and chemogenomic databases to assemble a three-dimensional inhibitor structure directly in the protein pocket. Target engagement by the inhibitors designed led to disruption of oncogenic phenotypes as demonstrated in enzymatic assays and in a panel of cancer cell lines, including acute lymphoblastic and myeloid leukemia (ALL/AML) and nonsmall cell lung cancer (NSCLC). Structural rationale underlying the approach was corroborated by X-ray crystallography. The lead compound demonstrated potent target inhibition in a pharmacodynamic study in leukemic mice.
Indexed as
Identifiers
What OpenQuestion holds
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.