Discovery of a Potent mTOR Inhibitor for Glioblastoma Cell Growth Suppression

Álvaro Lorente-Macías published a dataset on figshare in April 2026 detailing the discovery of a novel mTOR inhibitor. The data, stored in a 21.1 KB XLSX file, likely contains structure-activity relationships and screening results from iterative synthesis and testing against glioblastoma multiforme cell lines. The work identifies compound 3n (eALM1137) as a highly potent and selective candidate.

Use Cases

• Modeling structure-activity relationships (SARs) based on iterative compound synthesis and screening.
• Identifying chemical probe candidates based on high mTOR activity and selectivity data.
• Analyzing preclinical potential based on validation results from advanced glioblastoma stem cell models.

Strengths

• Data is derived from iterative design, synthesis, and screening cycles, suggesting a systematic approach.
• Preclinical validation was performed in advanced glioblastoma stem cell models.
• The dataset is focused on a specific, novel compound (3n/eALM1137) identified as best-in-class.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset's 21.1 KB size indicates a very limited scope, likely containing summary or key results rather than raw experimental data.

Provenance

Source

Álvaro Lorente-Macías via figshare.

Collection Method

Data likely generated through iterative synthesis of small compound libraries and screening against glioblastoma cell lines.

Time Range

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Freshness

Last updated 2026-04-13 04:34:22; freshness should be verified.

Geography

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