Identification and Characterizations of Novel, Selective Histone Methyltransferase SET7 Inhibitors by Scaffold Hopping- and 2D-Molecular Fingerprint-Based Similarity Search
Bibliographic record
Abstract
SET7, serving as the only histone methyltransferase that monomethylates ‘Lys-4’ of histone H3, has been proved to function as a key regulator in diverse biological processes, such as cell proliferation, transcriptional network regulation in embryonic stem cell, cell cycle control, protein stability, heart morphogenesis and development. What′s more, SET7 is involved inthe pathogenesis of alopecia aerate, breast cancer, tumor and cancer progression, atherosclerosis in human carotid plaques, chronic renal diseases, diabetes, obesity, ovarian cancer, prostate cancer, hepatocellular carcinoma, and pulmonary fibrosis. Therefore, there is urgent need to develop novel SET7 inhibitors. In this paper, based on DC-S239 which has been previously reported in our group, we employed scaffold hopping- and 2D fingerprint-based similarity searches and identified DC-S285 as the new hit compound targeting SET7 (IC50 = 9.3 μM). Both radioactive tracing and NMR experiments validated the interactions between DC-S285 and SET7 followed by the second-round similarity search leading to the identification ofDC-S303 with the IC50 value of 1.1 μM. In cellular level, DC-S285 retarded tumor cell proliferation and showed selectivity against MCF7 (IC50 = 21.4 μM), Jurkat (IC50 = 2.2 μM), THP1 (IC50 = 3.5 μM), U937 (IC50 = 3.9 μM) cell lines. Docking calculations suggested that DC-S303 share similar binding mode with the parent compoundDC-S239. What′s more, it presented good selectivity against other epigenetic targets, including SETD1B, SETD8, G9a, SMYD2 and EZH2. DC-S303 can serve as a drug-like scaffold which may need further optimization for drug development, and can be used as chemical probe to help the community to better understand the SET7 biology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".