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BiohacksAI

Evidence-Based Biohacking

Patent Pending
Domain Discovery

Metabolic Health & Glucose

Targets regulating glucose metabolism, insulin sensitivity, and lipid balance. Compounds are ranked by target influence (IDF-weighted), research rarity, and momentum — revealing under-studied candidates with active signal.

Targets:INSRPIK3CAAKT1PRKAA1PRKAA2PPARGPPARAPPARDSLC2A4NR1I2HNF4A
323 compounds matched · 11 targets active

Top Emerging Metabolic Health & Glucose Compounds

#CompoundTop TargetsScoreStudies
1BerberinecapPRKAA1 · PRKAA2 · PPARA16.140300
2Arachidonic AcidPPARA · PPARD · PPARG8.946245
3RutincapPRKAA2 · PPARG8.924300
4QuercetincapAKT1 · PRKAA2 · PPARG5.963300
5chrysincapPPARG · NR1I25.782300
6trequinsin hydrochloridePPARG · NR1I24.1921
7Luteolin 5,7,3',4'-tetrahydroxy-flavone,AKT1 · PPARG3.848150
8KaempferolscapPRKAA2 · PPARG · NR1I23.739300
9Linoleic AcidcapHNF4A · PPARA · PPARD3.531300
10RotenonecapPRKAA1 · PRKAA2 · PPARD3.150300
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50 compounds
#CompoundTargetsScoreStudiesEvid.
1
Berberinecap
7% human
PRKAA1PRKAA2PPARA
16.140
30047.9
2
8% human
PPARAPPARDPPARG
8.946
24547.4
3
Rutincap
2% human
PRKAA2PPARG
8.924
30041.5
4
1% human
AKT1PRKAA2PPARGNR1I2
5.963
30043.4
5
chrysincap
0% human
PPARGNR1I2
5.782
30039.8
6
PPARGNR1I2
4.192
17.6
7
1% human
AKT1PPARG
3.848
15040.6
8
PRKAA2PPARGNR1I2
3.739
30043.3
9
1% human
HNF4APPARAPPARDPPARG+1
3.531
30043.7
10
PRKAA1PRKAA2PPARDPPARG
3.150
30036
11
49% human
PRKAA1PRKAA2
3.044
1,00071.9
12
INSRPPARDPPARGNR1I2
2.764
30042.3
13
PRKAA1PRKAA2PPARA
2.690
30042.3
14
7% human
PPARAPPARDPPARG
2.599
11248.1
15
2% human
AKT1PIK3CANR1I2
2.568
30045.4
16
7% human
PPARAPPARDPPARGNR1I2
2.545
29747.1
17
AKT1
2.426
1127.5
18
PRKAA1PRKAA2
2.409
7640.3
19
17% human
INSRPRKAA1NR1I2
2.393
29846.7
20
5% human
PPARAPPARDPPARG
2.357
18444.1
21
0% human
NR1I2
2.350
26142.3
22
PIK3CA
2.341
1129.6
23
3% human
PPARG
2.326
30046.8
24
5% human
NR1I2
2.293
30045.1
25
NR1I2
2.293
30042.6
score = target_influence × rarity × momentum⚡ acceleration >2×cap = ≥300 studies (corpus ceiling)click target chip to filter

About Metabolic Health & Glucose Compound Discovery

Discover emerging metabolic health compounds via AMPK, PPAR-gamma, insulin receptor, and AKT1 target networks. The discovery score combines three signals: target influence (how strongly a substance interacts with metabolic health & glucose targets, weighted by target specificity via IDF), rarity factor (inverse log of study count — fewer studies means higher discovery potential), and momentum factor (recent publication acceleration). Compounds with high scores represent mechanistically relevant, under-researched candidates with active research interest.

Key targets in this domain include: INSR, IRS1, PIK3CA, AKT1, PRKAA1, PRKAA2. The analysis covers 323 compounds from the BiohacksAI corpus of 4,824 substances derived from PubMed-indexed bioassay data.