Small-Molecule Inhibitors: A Practical Guide for Research Selection — A Minimal Evidence Chain and Scenario-Based Validation Framework (Including a Selection Matrix, Case Cards, and Product Tables 1–4)

1. What Are Small-Molecule Inhibitors, and Why Must Research Talk About an “Evidence Chain”?

1) What is an “inhibitor”?

• In chemistry and biochemistry, an inhibitor refers to a substance that decreases the rate of a process; in enzyme systems, this commonly manifests as binding to the enzyme and thereby reducing the catalytic rate. In essence, the IUPAC definition of an inhibitor is “a substance that reduces a reaction rate.”

• A small-molecule inhibitor usually refers to a relatively low–molecular-weight compound—chemically synthesized (or derived from natural sources)—used to perturb the function of a specific protein in cells or in vivo (e.g., kinases, receptors, ion channels, metabolic enzymes), thereby altering pathway output or phenotype.

• Summary: An inhibitor is not a “magic molecule,” but a testable intervention tool: it must reduce the target function in the system in an explainable way.

Note: For the sake of research selection, this article uses “inhibitor” as a blanket term for tool-like small molecules that “reduce target functional output.” For receptors/ion channels and similar systems, stricter terminology may correspond to antagonists, blockers, or modulators, respectively.

2) Why must research talk about an “evidence chain”?

The most common pitfalls in experiments are:

• Seeing that an inhibitor “changes the phenotype,” then directly writing “the target causes the phenotype”;
• Fixating on a single IC₅₀/EC₅₀ number while ignoring whether the compound truly hits the target, has off-target activity, or actually reaches the site of action.

In real systems, phenotypic changes may arise from:

• Off-target effects (inhibiting related proteins in the same family);
• Insufficient exposure or precipitation (strong in vitro, ineffective in cells);
• Toxicity/stress responses (appears effective, but the system is collapsing);
• Chemical identity differences (salt form, enantiomer, purity, batch variation → poor reproducibility).

Therefore, research selection needs a clearly ordered minimal evidence chain: using the least information to maximally reduce misinterpretation and irreproducibility.

2. The Minimal Evidence Chain: A Selection Path from “Explainable” to “Reproducible”

When you obtain an inhibitor (or are preparing to purchase one), it is recommended to walk through Step 1→7 once:

• Steps 1–4: lock down whether the effect can be attributed to the intended target (identity, biochemical potency, cellular on-target evidence, selectivity/orthogonal controls).
• Steps 5–7: make the result reproducible and robust in your system (exposure/solubility & precipitation, key risks, batch-to-batch and supply consistency).

Step	Core checkpoint	Information to confirm/obtain	Likely consequence if skipped	Common pitfall
Step 1	Chemical identity consistency	Structure, purity, salt form/hydrate, stereochemistry (enantiomer/ratio), batch information	“Same name, different substance” → non-comparable activity/solubility/exposure; batch differences → poor reproducibility	Concluding from name/CAS only; not specifying salt form and enantiomer
Step 2	In vitro target activity is real	IC₅₀/Ki/Kd or functional readout; key assay conditions (substrate/ATP, time, readout); IC₅₀ is not a “compound constant”	Cannot distinguish “target not real” vs “system mismatch”; negatives later cannot be diagnosed	Hard-comparing a single IC₅₀ across different assay contexts
Step 3	Cellular on-target evidence	Target engagement (occupancy/binding) or dose/time response of proximal on-target markers; rescue experiments when needed	Phenotype cannot be attributed: off-target/toxicity/stress can masquerade as “effective inhibition”	Looking only at phenotype without building a target-linked chain
Step 4	Selectivity and off-target constraints	Same-family / profiling panels; key off-targets; orthogonal compounds (different scaffolds) consistency	Mechanistic interpretation becomes uncontrolled: effects may come from inhibiting multiple targets in parallel	Treating high-dose “spray-and-pray inhibition” as a target-specific effect
Step 5	Exposure and usability meet requirements	Solubility/precipitation, stability, permeability/efflux; (CNS) brain exposure; clearance/half-life	When in vitro is strong but cells/in vivo fail, you cannot tell “insufficient potency” vs “insufficient reach”	Ignoring precipitation/adsorption; blindly increasing concentration
Step 6	Key risks are controllable	CYP TDI/metabolic inhibition, hERG/toxicity flags, reactivity, aggregation/signal interference risks	More false positives and unexplained side effects; higher failure rate in vivo/long-term dosing	Mistaking “strong effect” for “strong mechanism”
Step 7	Sustainable access and batch consistency	Route risk points, starting material availability, scale-up and batch consistency, supply chain	Hard to reproduce or extend long-term; unstable supply of key molecules can derail projects	Looking only at a one-off sample without assessing future availability

Supplement:

Step 4 — Orthogonal compound (different scaffold) consistency: Priority: (1) same target, different scaffold (orthogonal scaffold) consistency; (2) inactive controls (e.g., clearly SAR-defined inactive analogs/enantiomers); (3) genetic consistency.

3. Selection Matrix (Use × Target Class): Priority Evidence and Key Readouts

Experimental task (use)	Kinase inhibitors (SIK/IRAK4/CK1δ)	Non-kinase enzyme inhibitors (e.g., EBP)	Ion channel inhibitors (e.g., ClC-1)
A Causal validation (does the target drive the phenotype?)	Priority evidence: same-family/kinome selectivity + dose curve of proximal on-target markers in cells; ideally with orthogonal compounds/inactive controls	Priority evidence: direct target engagement (substrate/metabolite + dose×time closure) + rescue/controls	Priority evidence: functional readouts (current/excitability) with a dose window consistent with phenotype + subtype/tissue selectivity
B Pathway positioning (involvement; upstream/downstream mapping)	Key readouts: time course of p-substrates; avoid high-dose “spray”; confirm trend with a second scaffold	Key readouts: proximal metabolic readouts first; distal phenotype only as supporting evidence	Key readouts: electrophysiology/functional readouts first; phenotype interpretation must be anchored to channel function changes
C In vivo / tissue reach (CNS/in vivo/tissue exposure)	Prerequisite (pass before mechanism): confirm tissue/brain exposure (ideally unbound/available brain concentration) and clearance/half-life can cover the in vitro potency range; then discuss selectivity and attribution	Prerequisite (pass before phenotype): confirm in vivo exposure is controllable and not excessively accumulating (longer half-life is not always better), and demonstrate engagement via proximal chemical/metabolic biomarkers; then discuss distal phenotype	Prerequisite (pass before clinically relevant phenotype): confirm tissue/subtype selectivity and a “functional dose window” (often partial inhibition is sufficient); then map channel function changes to phenotype

4. Five High-Frequency Scenarios: A “Three-Step Minimal Validation Path” for Inhibitor Selection

High-frequency scenario (use + target type)	Step 1 | Evidence check	Step 2 | Key readouts	Step 3 | Control constraints	Pass criterion (one sentence)	Corresponding case card
Causal validation × kinase (e.g., SIK/IRAK4/CK1δ)	Same-family/kinome selectivity data? Proximal on-target markers in cells?	Dose–response of proximal on-target markers (with viability/stress baseline in parallel)	Consistency via orthogonal compounds or inactive controls; keep concentrations within the selectivity window	“Phenotype tracks proximal on-target markers and is reproducible within the selectivity window.”	Card 1 (GLPG3970)
Property gate (CNS/in vivo) × kinase	Brain exposure/available concentration (Kp,uu, etc.) or clear CNS penetration evidence?	Establish in vivo/brain PD/occupancy or proximal pathway readout	Cross-check “unbound brain concentration vs in vitro potency/selectivity” for consistency	“Unbound brain exposure covers potency without being ‘forced’ by off-target-level dosing.”	Card 2 (BIO-7488) / Card 5 (CK1δ)
Causal validation × non-kinase enzyme (e.g., metabolic enzyme EBP)	Direct TE path exists (substrate/metabolite/proximal markers)?	Couple “dose × time”: proximal metabolic readouts change predictably with dose/time	Add bypass/rescue or exclude alternative explanations (at least ensure “proximal changes first, distal phenotype later”)	“TE closure holds, and phenotype is supported by proximal evidence.”	Card 3 (EBP 11)
Property gate × non-kinase enzyme (in vivo usability/exposure shape)	Exposure shape (clearance/half-life/oral) and accumulation risk cues?	Verify engagement in vivo using proximal metabolic biomarkers (not distal phenotype alone)	Check whether “exposure shape fits the purpose” (half-life is not always better when longer)	“In vivo engagement is reproducible and exposure shape is controllable.”	Card 3 (EBP 11)
Causal/pathway positioning × ion channel (e.g., ClC-1)	Channel subtype/tissue selectivity info? Functional dose-window data?	Do functional readouts first (electrophysiology/excitability), then connect to phenotype	Constrain safety/efficacy window for partial inhibition; avoid full blockade	“Functional readouts match phenotype and a controllable dose window exists.”	Card 4 (NMD670)

5. Five Case Cards

Card 1 | GLPG3970 (SIK2/SIK3): For Causal Validation, You Must “Inhibit Precisely” Within the Family

1. Applicable scenario: Causal validation × kinase (within-family kinases: SIK1/2/3)

2. Core question of this card:

• When the target belongs to a kinase family, how do you avoid “inhibiting multiple family members at once,” making the phenotype non-attributable?

3. Key evidence to check first

• Within-family selectivity: Is it strong on SIK2/3 and relatively weaker on SIK1? (GLPG3970 is described in the literature as a dual SIK2/SIK3 inhibitor with selectivity over SIK1, with IC₅₀-scale differences reported.)
• Cellular proximal on-target readout: A proximal SIK-pathway readout shows dose dependence.
• Attribution constraints: At least one “second ruler” exists (orthogonal compound / inactive control / genetic consistency).

4. Recommended minimal action path (tune to your system)

• Start with dose–response of proximal readouts (faster to confirm “hit the target” than endpoint phenotype first).
• Keep working concentrations within the window where selectivity still holds (avoid sweeping SIK1 at high dose).
• Constrain by consistency: use a different-scaffold SIK inhibitor or a genetic approach to validate directionality.

5. Signals to treat cautiously (check first)

• Phenotype appears only at clearly high concentrations, while proximal readouts do not change with dose as expected; or the “effective concentration” clearly exceeds the within-family selectivity window.

6. Primary reference

Peixoto C, et al. Journal of Medicinal Chemistry (2024). DOI: 10.1021/acs.jmedchem.3c02246.

Card 2 | BIO-7488 (IRAK4): In CNS Projects, Prove “It Reaches the Brain” Before Talking Mechanism

1. Applicable scenario: In vivo/tissue reach (CNS) × kinase (IRAK4; CNS inflammation–related application context)

2. Core question of this card:

• In CNS projects, “strong in vitro” ≠ “usable in the CNS.” How do you constrain conclusions to brain-available exposure and CNS-relevant PD chains, rather than forcing mechanism with an invalid exposure window?

3. Key evidence to check first

• CNS penetration / brain exposure evidence: Is it explicitly described as CNS-penetrant, with PK/exposure information that supports a CNS-relevant conclusion (at least enough to support “it gets into the brain”)?
• Brain/CNS-relevant proximal PD readouts: Are there IRAK4-pathway proximal readouts (not only distal phenotypes) linked to dose/time?
• Selectivity/concentration constraints: Is there a selectivity baseline or panel information, or at least an argument that effects are not seen only by “pushing to very high concentrations”?

4. Recommended minimal action path (tune to your system)

• Ask exposure first: if CNS exposure info is missing, treat it as an in vitro/pathway tool—not a CNS causal tool.
• Build CNS-relevant proximal PD: in CNS-relevant cells/tissues/models, establish dose–response or time–response of an IRAK4 proximal readout.
• Constrain interpretation by “available exposure covers potency”: align your used dose/concentration to brain-available exposure; avoid off-target-level explanations.

5. Signals to treat cautiously (check first)

• Effects appear only at clearly high doses/concentrations that cannot be supported by exposure evidence; or proximal PD and phenotype are decoupled (PD unchanged, phenotype changes).

6. Primary reference

Evans R, et al. Journal of Medicinal Chemistry (2024). DOI: 10.1021/acs.jmedchem.3c02226.

Card 3 | EBP Inhibitor 11: In Metabolic/Biosynthetic Pathways, First Make TE (Proximal Evidence) Closure “Hard”

1. Applicable scenario: Causal validation × non-kinase enzyme (EBP; cholesterol biosynthesis pathway context; often linked to in vivo/brain reach)

2. Core question of this card:

For non-kinase enzymes/metabolic pathways, distal phenotypes are easily explained by bypass routes. How do you lock down “the target is truly inhibited” using target engagement (TE) closure, then discuss phenotypic attribution?

3. Key evidence to check first

• Proximal TE path: Can EBP engagement be reflected by substrate/metabolite or more proximal chemical/biological markers (rather than distal phenotype only)?
• Dose × time coupling: Do proximal indicators change regularly with dose and time (forming a closed-loop evidence chain)?
• Constraints on alternative explanations: At minimum, satisfy the sequential logic “proximal changes first, distal phenotype later”; add rescue/bypass controls when needed to reduce ambiguity.

4. Recommended minimal action path (tune to your system)

• Prioritize proximal readouts: select substrate/metabolite/proximal markers as primary readouts instead of jumping to endpoint phenotype.
• Run a small dose×time matrix: 2–3 doses × 2–3 time points to harden the trend (more reliable than single-point readouts).
• Constrain causality via sequence logic: prove TE closure first, then ask whether the distal phenotype can be reasonably explained by that TE.

5. Signals to treat cautiously (check first)

• Distal phenotype changes strongly, but proximal TE indicators do not change predictably with dose/time (suggesting bypass, stress, or non-specific effects).

6. Primary reference

Dorel R, et al. Journal of Medicinal Chemistry (2024). DOI: 10.1021/acs.jmedchem.3c02396.

Card 4 | NMD670 (ClC-1): For Channels, Start with the “Functional Dose Window,” Then Connect Function to Phenotype

1. Applicable scenario: Causal/pathway positioning × ion channel (ClC-1; skeletal muscle excitability and myasthenia-related context)

2. Core question of this card:

A common misuse of ion-channel inhibitors is “phenotype first, function later.” How do you lead with functional readouts and a controllable dose window (often partial inhibition), so phenotypic explanations rest on verifiable channel-function changes?

3. Key evidence to check first

• Functional readout evidence: Are electrophysiology/excitability functional readouts available and dose-responsive?
• Controllable dose window: Is it emphasized or supported that partial inhibition is sufficient, rather than pursuing full blockade (critical for safety and interpretability)?
• Tissue/subtype selectivity: Is tissue specificity or subtype selectivity addressed, avoiding pushing the system into a high-risk “full blockade” regime?

4. Recommended minimal action path (tune to your system)

• Run dose–response for functional readouts first (≥5 doses) to define the effective range and upper bound.
• Then map functional change to phenotype: keep the order “function → phenotype,” not the reverse.
• Constrain dosing within a “partial-inhibition window”: validate within an explainable, controllable window rather than inferring from full-block concentrations.

5. Signals to treat cautiously (check first)

• Phenotype appears only at near-saturating/full-block high concentrations, accompanied by clear non-specific responses or system instability.

6. Primary reference

Skov M, et al. Science Translational Medicine (2024). DOI: 10.1126/scitranslmed.adk9109.

Card 5 | CK1δ Inhibitor (Janssen): Check Whether Selectivity Evidence Is “Hard Enough” Before Discussing In Vivo/Brain Reach

1. Applicable scenario: Causal validation × kinase or in vivo/brain reach × kinase (CK1δ; tool-compound context aiming for “high selectivity + brain penetration/in vivo usability”)

2. Core question of this card:

• To use a kinase inhibitor as a “high-confidence tool compound,” the core prerequisite is often whether selectivity evidence is sufficiently hard (panels/structure/binding mode). If used for CNS/in vivo, exposure and engagement chains are also needed to lock down interpretation space.

3. Key evidence to check first

• Selectivity support: Is there a selectivity panel (kinome or equivalently strong evidence) and a clear structural/binding-mode design logic explaining selectivity?
• (If used for CNS) brain exposure and engagement: Are brain exposure and target engagement data provided to support its status as a CNS tool compound?
• Cell/tissue proximal readouts: Are there proximal on-target readouts to close the “in vitro/cellular” chain, avoiding distal-phenotype-only inference?

4. Recommended minimal action path (tune to your system)

• Verify selectivity evidence is complete: without panel/structural support, downgrade claim strength (better suited for pathway hinting than causal attribution).
• Run dose–response for proximal readouts: confirm regular responses within a controllable concentration range.
• If used for CNS/in vivo: constrain dosing and interpretation by “available exposure covers potency and the selectivity window,” avoiding off-target concentrations.

5. Signals to treat cautiously (check first)

• Insufficient selectivity evidence but making direct “target causality” claims; or, in CNS scenarios, lacking exposure/engagement chains yet making mechanistic attribution.

6. Primary reference

McCarver S, et al. ACS Medicinal Chemistry Letters (2024). DOI: 10.1021/acsmedchemlett.3c00523.

Summary

These five cards cover the most common inhibitor-use scenarios in research:

• Within-family kinases: control the selectivity window first, then infer causality;
• CNS/in vivo: pass “reach and residence” first, then talk mechanism;
• Metabolic enzymes: close the target engagement (proximal evidence) loop first, then interpret distal phenotypes;
• Ion channels: lead with functional readouts and find a controllable dose window;
• High-confidence tool compounds: verify selectivity evidence is hard enough before making strong conclusions, then constrain interpretation with exposure/engagement chains for CNS/in vivo use.

Key points:

• The core of causal validation is not “the phenotype changed,” but “on-target evidence + selectivity constraints.”
• The core of in vivo/CNS is not “stronger in vitro,” but “available exposure covers potency and the selectivity window.”
• For channels and metabolic enzymes, the core is not “mechanism is more complex,” but “readouts must be closer to target/function.”

6. Product Selection Navigation Table | Small-Molecule Inhibitors: Quickly Locate Tables 1–4 by Experimental Task

Need / Scenario	Which table to check first	Why this table is the best fit	What you can quickly find in this table
You already have a pathway hypothesis and want a fast check of whether an ERK/p38/PI3K–AKT–mTOR/ROCK/JAK axis is driving the phenotype (“add the drug and you’ll know”)	Table 1	Signaling Pathway & Targeted-Therapy Inhibitors	Table 1 organizes mainstream inhibitors by targets / pathway nodes, making it ideal for causal validation and node positioning (upstream / midstream / downstream controls)
You need clinical-target inhibitor controls / drug-sensitivity benchmarking: do “clinically relevant targets” such as EGFR, BCR-ABL, BRAF, BTK, CDK4/6, PARP hold up in your model?	Table 1	Signaling Pathway & Targeted-Therapy Inhibitors	Table 1 concentrates clinical-target inhibitors + canonical pathway inhibitors, suitable for drug-sensitivity controls, target-dependency validation, and quick combination-strategy prescreens
Your project is epigenetics / transcriptional dependency: you want to test whether “acetylation / bromodomains / deacetylation” is a key knob, or build inhibitor combinations for transcriptional reprogramming	Table 2	Epigenetics & Proteostasis-Regulating Inhibitors	Table 2 groups transcription-layer “control knobs” such as HDAC / BET / Sirtuin for fast directional validation and combination controls
You suspect the phenotype comes from protein degradation / proteostasis (UPS / proteases / lysosome-related): you need to decide “less synthesis vs faster degradation,” or protect proteins during lysis/purification	Table 2 (proteasome/proteases) + Table 3 (autophagy/lysosome)	Table 2 is more UPS/protease inhibition; Table 3 focuses more on organelles and flux steps. Together they help deconvolute degradation routes	MG-132 (proteasome); E-64 (cysteine proteases); bortezomib-class; plus bafilomycin A1 / chloroquine in Table 3 (lysosomal acidification/flux)
You need to interpret autophagy flux: distinguish “autophagy induced” vs “degradation blocked,” or validate flux under mTOR inhibition	Table 3	Cellular-Process Tool Compounds (incl. Autophagy–Lysosome) + (paired with) rapamycin in Table 1	Autophagy experiments often misread “LC3 increase” as “more autophagy.” Table 3 provides terminal blockers and flux tools; rapamycin in Table 1 strengthens induction-end controls
You need cell-cycle / mitosis / cytoskeleton controls: e.g., proliferation drops—does it reflect “pathway inhibition” or direct disruption of microtubules/cycle?	Table 3	Cellular-Process Tool Compounds	Table 3 concentrates microtubule and cell-cycle tools, ideal for mechanism-triage controls to avoid mistaking cytotoxicity for target effects
You’re testing transcription/translation dependency: does the phenotype require new transcription? Is protein decrease due to reduced synthesis or faster degradation?	Table 3	Cellular-Process Tool Compounds	Table 3 provides “transcription-off / translation-off” tools for minimal causal dissection (especially when paired with inhibitor-treatment time windows)
Your phenotype involves secretion / membrane trafficking / Golgi processes (receptor surface delivery, secreted factor dependence, surface protein changes), and you worry the inhibitor phenotype may actually be transport-related	Table 3	Cellular-Process Tool Compounds	Table 3 includes ER–Golgi transport blockers for quick “transport dependency” triage controls
You want inhibitor experiments to be “more stable”: preserve phosphorylation after lysis, keep LC–MS conditions consistent, make sample prep reproducible, improve solubility of hydrophobic inhibitors	Table 4	General Supporting & Protective Reagents	Table 4 is the reproducibility “infrastructure”: buffers/volatile additives, phosphorylation protection, solubilizers, thiol trapping, cofactors
You suspect the inhibitor triggers ROS/electrophilic stress/reactive metabolites and need to rule out “off-target chemical reactions”	Table 4	General Supporting & Protective Reagents (paired with Table 2/Table 1 readouts)	Table 4 provides capture/buffering/cofactors to quickly build reactivity-risk controls and avoid mistaking chemical stress for on-target effects

Usage recommendation:

If your goal is to “locate the pathway/target first,” start with Table 1. If you suspect the phenotype comes from “epigenetics/proteostasis,” go to Table 2. If you need “cell-process mechanism deconvolution (cell cycle/autophagy/transport/transcription–translation),” go to Table 3. If you want to stabilize reproducibility or run reactivity checks, prioritize Table 4.

Table 1 | Signaling Pathway & Targeted-Therapy Inhibitors (Kinases / DDR / Oncology Targets)

Category	CAS No.	Aladdin Cat. No.	Name	Spec / Purity	Product features / selection notes (small-molecule inhibitor experiments)
Kinase-pathway inhibitor | MAPK/ERK axis	167869-21-8	P276564	PD 98059 (DMSO solution)	Moligand™, 10 mM	MEK1 inhibitor (classic ERK-pathway tool compound). A 10 mM DMSO pre-made stock enables direct dilution for cell assays; useful for quickly building an “ERK dependency” causal chain and dose series.
Kinase-pathway inhibitor | MAPK/ERK axis	391210-10-9	P125494	PD0325901, MEK1/2 inhibitor	Moligand™, ≥99%	High-potency MEK1/2 inhibitor; used for strong ERK-pathway suppression and resistance models (can form “different generation / different binding-site” controls together with PD98059 and U0126).
Kinase-pathway inhibitor | MAPK/ERK axis	109511-58-2	U274745	U0126, MKK inhibitor	Moligand™, ≥98%	Common MEK1/2 inhibitor (ERK-pathway tool). Running in parallel with PD98059/PD0325901 improves robustness (different chemotypes/inhibition profiles).
Kinase-pathway inhibitor | p38 MAPK	152121-30-7	S134307	SB 202190, p38 MAPK inhibitor	Moligand™, ≥99%	Classic p38 inhibitor; for inflammatory cytokines, stress signaling, and differentiation-related phenotype validation; pairing with SB-203580 reduces “single-molecule bias.”
Kinase-pathway inhibitor | p38 MAPK	152121-47-6	S131899	SB-203580, p38 MAPK inhibitor	Moligand™, ≥98% (HPLC)	Classic p38α/β inhibitor; often used with SB202190 as a “double-probe strategy” to strengthen causal judgment of p38 dependency.
Kinase-pathway inhibitor | PI3K	154447-36-6	L124970	2-Morpholinyl-8-phenylchromone	Moligand™, ≥98%	Common PI3K inhibitor (LY294002-class); for pathway blockade and phenotype validation along the PI3K–AKT–mTOR axis (can form “different nodes / different chemotypes” controls with MK-2206 and wortmannin).
Kinase-pathway inhibitor | PI3K	19545-26-7	W100984	Wortmannin	Moligand™, ≥98%	Classic PI3K inhibitor (irreversible / strong tool compound); suitable as a “hard block” control. Pairing with LY294002/MK-2206 helps localize the node within the axis.
Kinase-pathway inhibitor | AKT	1032350-13-2	M129684	MK-2206 2HCl	Moligand™, ≥98%	Allosteric AKT inhibitor; blocks a midstream node in the PI3K–AKT axis. Together with PI3K inhibitors (LY294002/wortmannin), enables “upstream vs midstream” comparisons to trace phenotype origin.
Proteostasis/signaling inhibitor | mTORC1	53123-88-9	S115842	Rapamycin	Moligand™, ≥98% (HPLC)	mTORC1 inhibitor; for autophagy, metabolic reprogramming, and growth-signal validation; often combined with bafilomycin A1 (blocks lysosomal acidification) for autophagy-flux interpretation.
Kinase-pathway inhibitor | ROCK	146986-50-7	Y125330	Y-27632	Moligand™, ≥98%	ROCK inhibitor; widely used for cytoskeleton/contractility phenotypes and for improving survival during passaging of stem/primary cells (note impact on adhesion/migration readouts).
Targeted-therapy inhibitor | JAK	477600-75-2	T122330	Tofacitinib (CP-690550)	Moligand™, ≥98% (HPLC)	JAK inhibitor; for cytokine–STAT pathway suppression and immune/inflammation models; suitable as a control set with ruxolitinib.
Targeted-therapy inhibitor | JAK1/2	941678-49-5	R126338	Ruxolitinib (INCB018424)	Moligand™, ≥98%	JAK1/2 inhibitor; for cytokine–STAT axis validation in inflammation/immune models; together with tofacitinib provides “different JAK preference/spectrum” controls.
Targeted-therapy inhibitor | CDK4/6	571190-30-2	P126849	Palbociclib	Moligand™, ≥99%	CDK4/6 inhibitor; for G1 arrest and RB-pathway dependency validation; commonly paired with proliferation readouts (EdU/Ki67/cell-cycle flow cytometry).
Targeted-therapy inhibitor | BCR-ABL/c-KIT	152459-95-5	I124963	Imatinib	Moligand™, ≥99%	Representative tyrosine kinase inhibitor (BCR-ABL/c-KIT/PDGFR); used for oncogenic signaling dependency validation and as a positive control; complementary to “imatinib mesylate” regarding solubility and formulation convenience.
Targeted-therapy inhibitor | BCR-ABL/c-KIT (salt form)	220127-57-1	I129225	Imatinib mesylate	≥99%	Salt form of imatinib: often easier for weighing/dissolution and more stable stock preparation; can be used as “same molecule, different salt” control or chosen for more convenient formulation in a given system.
Targeted-therapy inhibitor | EGFR	184475-35-2	G125799	Gefitinib (ZD1839)	Moligand™, ≥99%	Classic EGFR inhibitor; suitable for pathway blockade and drug-sensitivity controls in EGFR-driven cell lines (paired with erlotinib for same-target different-molecule control).
Targeted-therapy inhibitor | EGFR	183321-74-6	E126633	Erlotinib	Moligand™, ≥98%	EGFR inhibitor; together with gefitinib forms “same target, different molecule” controls, helping exclude single-chemotype bias and validate EGFR dependency.
Targeted-therapy inhibitor | BTK	936563-96-1	P127143	Ibrutinib (PCI-32765)	Moligand™, ≥98%	BTK inhibitor; for BCR signaling, immune-cell activation, and B-cell malignancy models; useful as a BTK-dependency positive control.
Targeted-therapy inhibitor | BRAF (V600E)	918504-65-1	V127521	Vemurafenib (PLX4032, RG7204)	Moligand™, ≥98%	BRAF(V600E) inhibitor; for MAPK-dependent tumor models and resistance-mechanism studies; suitable as a BRAF-mutation dependency positive control.
DNA repair inhibitor | PARP	763113-22-0	O126162	Olaparib (AZD2281, Ku-0059436)	Moligand™, ≥98%	PARP inhibitor; for DDR dependency validation, combination studies with platinum/radiotherapy/DDR pathways, and synthetic lethality (BRCA/HRD) models.
Broad-spectrum kinase inhibitor | positive control	62996-74-1	S102392	Staurosporine	Moligand™, ≥98%	Classic broad-spectrum protein kinase inhibitor; often used as a “strong inhibition/apoptosis induction” positive control or assay QC (not suitable for precise target attribution).
Kinase-pathway inhibitor | JNK	129-56-6	S125267	Anthra[1,9-cd]pyrazol-6(2H)-one	Moligand™, ≥98%	This scaffold is commonly used as a JNK inhibitor tool compound (SP600125-class); for stress–inflammation–apoptosis pathway validation (note frequent off-panel effects; orthogonal controls are recommended).

Table 2 | Epigenetics & Proteostasis-Regulating Inhibitors (Sirtuin / HDAC / BET / Proteasome / Proteases)

Category	CAS No.	Aladdin Cat. No.	Name	Spec / Purity	Product features / selection notes (small-molecule inhibitor experiments)
Epigenetics/metabolism | NAD-related tool compound	98-92-0	N108087	Nicotinamide	For cell culture; for insect cell culture; ≥99.5% (HPLC)	An NAD⁺-related metabolite; in epigenetics it is a classic tool inhibitor/modulator for Sirtuin (SIRT) deacetylases (also affects multiple NAD⁺-dependent processes). Suitable for quick perturbation of the “deacetylation axis” or supplementation controls.
Epigenetic inhibitor | HDAC	149647-78-9	V125336	N-Hydroxy-N′-phenyloctanediamide	Moligand™, ≥99%	Common HDAC-inhibitor scaffold (SAHA/vorinostat class); for epigenetic regulation, transcriptional reprogramming, and proliferation-inhibition studies; suitable to validate “acetylation ↑ / HDAC dependency.”
Epigenetic inhibitor | HDAC	58880-19-6	T129665	Trichostatin A (TSA)	Moligand™, ≥98%	High-potency HDAC inhibitor; for acetylation upregulation and transcriptional remodeling; can be paired with SAHA as a “strong vs relatively milder” control.
Epigenetic inhibitor | BET bromodomains	1268524-70-4	J166817	(+)-JQ1, BET bromodomain inhibitor	≥98% (HPLC)	Classic BET (BRD2/3/4) bromodomain inhibitor; for validating transcriptional dependency and super-enhancer–associated target genes; often combined with HDAC inhibitors as epigenetic combination controls.
Protease inhibitor | cysteine proteases	66701-25-5	E109034	E-64, cysteine protease inhibitor	Moligand™, ≥99%, protease inhibitor	Typical irreversible cysteine protease inhibitor; commonly used to suppress proteolysis during lysis/purification and stabilize targets, or as a control component in lysosome/proteasome-related studies.
Proteostasis inhibitor | proteasome	133407-82-6	M126521	MG-132, reversible proteasome inhibitor	Moligand™, ≥98%	Classic reversible proteasome inhibitor; for accumulating ubiquitinated proteins, validating UPS-dependent degradation, and cross-controls with autophagy pathways (note possible cross-inhibition of other proteases).
Proteostasis inhibitor | proteasome	179324-69-7	B125789	Bortezomib (PS-341)	Moligand™, ≥98%	Representative proteasome inhibitor; for blocking UPS degradation, rapidly accumulating ubiquitinated/short-lived proteins, and testing whether phenotypes depend on proteasome-mediated degradation. Often used as an orthogonal control vs MG-132 (different chemotype) or combined with autophagy/lysosome inhibitors to partition “proteasome vs lysosome” contributions.

Table 3 | Cellular-Process Tool Compounds (Cytoskeleton / Cell Cycle / Transcription / Translation / Trafficking / Autophagy–Lysosome / Ca²⁺ Signaling)

Category	CAS No.	Aladdin Cat. No.	Name	Spec / Purity	Product features / selection notes (small-molecule inhibitor experiments)
Cell culture tool | selection pressure / translation-related	58-58-2	P432998	Puromycin	For cell culture; Ready Made Solution; from Streptomyces alboniger; 10 mg/mL in H₂O	Common antibiotic for selection: used to select puromycin-resistant stable lines after lentiviral transduction; can also be used for protein-synthesis readouts such as SUnSET (note toxicity window and cell line–dependent sensitivity).
Cell culture tool | transcription inhibition / half-life assays	50-76-0	A432787	Actinomycin D	For cell culture; from Streptomyces sp.	Classic transcription inhibitor; used for RNA half-life and transcription-dependency checks (e.g., whether inhibitor-induced phenotypes require new transcription). Highly toxic; best for short-time, low-dose window experiments.
Cell culture tool | translation inhibition / CHX chase	66-81-9	C729197	“3-[2-(3,5-Dimethyl-2-oxocyclohexyl)-2-carboxyethyl]glutaramide”	Moligand™, ≥98%	A typical translation elongation inhibitor (Cycloheximide, CHX): used for CHX-chase protein half-life measurements and to separate “synthesis vs degradation” contributions. Strong inhibition—mind time window and toxicity.
Cytoskeleton / mitosis tool	64-86-8	C106740	Colchicine	Moligand™; for plant cell culture; ≥98% (HPLC)	Tool compound that inhibits microtubule polymerization and disrupts the spindle; used for cell-cycle blockade and mitosis-related pathway interrogation (pairs with paclitaxel as “microtubule inhibition vs stabilization” control).
Cytoskeleton / mitosis tool	33069-62-4	P106868	Paclitaxel	Moligand™; analytical standard; ≥99%	Microtubule stabilizer; widely used as a tool for cell cycle/apoptosis pathways and as a positive control to benchmark “inhibitor-induced proliferation inhibition/cell death.” Forms microtubule-control sets with colchicine/nocodazole.
Cytoskeleton / mitosis tool	31430-18-9	N129755	Nocodazole	≥98%	Microtubule depolymerizer; used for cell-cycle synchronization, spindle checkpoint studies, and mitotic arrest—very practical for deconvoluting mechanisms behind “inhibitor affects proliferation/cell cycle.”
Secretion/trafficking tool | ER–Golgi transport inhibition	20350-15-6	B102375	Brefeldin A	≥98% (HPLC); from Penicillium brefeldianum	Classic ER–Golgi transport/secretion inhibitor; used to test whether phenotypes depend on secretion/membrane trafficking and Golgi function—commonly applied in receptor trafficking and secreted factor–dependent inhibitor studies.
Lysosome/autophagy tool | weak-base accumulation	50-63-5	C129284	Chloroquine phosphate	≥99%	A weak base that accumulates in lysosomes: commonly used to inhibit lysosomal acidification and perturb autophagy endpoints/endocytosis–lysosome pathways; suitable as a control for “autophagy flux / lysosome dependency” (note broad cellular effects).
Lysosome/autophagy flux tool | V-ATPase	88899-55-2	B101389	Bafilomycin A1	≥95%	V-ATPase inhibitor: blocks lysosomal acidification and autophagosome–lysosome degradation; often combined with rapamycin/chloroquine to distinguish “autophagy induction vs flux blockade.”
Ca²⁺ signaling / stress tool | SERCA	67526-95-8	T135258	Thapsigargin	Moligand™, ≥95%	A classic SERCA (sarcoplasmic/endoplasmic reticulum Ca²⁺-ATPase) inhibitor that triggers ER Ca²⁺ store release and ER stress; widely used for Ca²⁺ signaling and UPR/apoptosis pathway perturbation.

Table 4 | General Supporting & Protective Reagents (LC–MS / Buffer Salts / Phospho-Protection / Solubilization / Thiols & Cofactors)

Category	CAS No.	Aladdin Cat. No.	Name	Spec / Purity	Product features / selection notes (small-molecule inhibitor experiments)
Analysis/prep | pH adjustment & volatile additive	64-19-7	A116166	Glacial acetic acid	GR; ≥99.5%	Commonly used for buffer preparation/pH adjustment, reaction quenching, or acidifying samples before injection; useful in inhibitor sample prep (protein precipitation by acidification / improved reproducibility) and as a volatile acid source in some LC conditions.
Analysis/prep | volatile salt for LC–MS	631-61-8	A433169	Ammonium acetate solution	Pharmaceutical grade; PharmPure™; for IVD application	Typical volatile buffer salt; used for LC–MS mobile phases/sample buffering and ionic-strength control; common in general workflows for inhibitor quantitation, metabolite analysis, and proteomics sample processing.
Analysis/prep | common LC–MS acid	64-18-6	F433212	Formic acid (FA)	Pharmaceutical grade; PharmPure™; ≥98%	One of the most commonly used volatile acids in LC–MS; acidifies mobile phases, improves peak shape/ESI response; also used to acidify/terminate samples after inhibitor treatment and to control injection consistency.
Analysis/prep | volatile salt for sample systems	540-69-2	A432863	Ammonium formate	Anhydrous; reagent grade; ≥97%	Common volatile buffer salt (MS-friendly); suitable when mild buffering and MS compatibility are needed in inhibitor exposure–metabolomics/proteomics/PK sample workflows.
Proteomics/sample prep | volatile buffer	1066-33-7	A110536	Ammonium bicarbonate	Reagent grade	Classic digestion buffer in proteomics (commonly used for trypsin digestion); a general buffer system for sample prep in “inhibitor treatment → proteomics/phosphoproteomics” workflows.
Delivery/solubilization | solubilizing excipient	128446-35-5	C485578	HP-β-cyclodextrin (HPB)	PharmPure™, USP	HP-β-cyclodextrin: common solubilizer/delivery excipient for hydrophobic small molecules (many kinase inhibitors, natural product–like inhibitors, etc.); useful for aqueous working solutions, animal dosing, or reducing organic solvent percentage.
Lysis/protection | phosphatase inhibitor additive	7681-49-4	S111591	Sodium fluoride (NaF)	GR; ≥98%	One of the classic lysis/phosphorylation-protection components (inhibits multiple Ser/Thr phosphatases). Often used with orthovanadate, pyrophosphate, β-glycerophosphate, etc. to prevent signal loss after inhibitor treatment.
Lysis/protection | phosphatase inhibitor additive	7722-88-5	S108847	Sodium pyrophosphate	AR; ≥99%	Common lysis additive: inhibits multiple phosphatases and protects phosphorylation states; suitable for sample prep for WB/phospho-readouts after inhibitor treatment.
Lysis/protection | phosphatase inhibitor additive	13408-09-8	D301908	Sodium β-glycerophosphate solution	0.5 M	Classic Ser/Thr phosphatase inhibitor/substrate mimic; used to protect phosphorylation signals and improve reproducibility in pathway-inhibition experiments.
Lysis/protection | key additive for pTyr protection	13721-39-6	S105283	Sodium orthovanadate	≥99%	Classic protein tyrosine phosphatase (PTP) inhibitor; together with NaF/pyrophosphate/β-glycerophosphate forms a core set for maintaining phosphorylation during “inhibitor–pathway validation.”
Redox/reactivity assessment | thiol trapping	52-90-4	L755721	L-Cysteine	UltraBio™, ≥98.5% (RT)	Common “thiol/nucleophile” control for evaluating electrophilic inhibitors or reactive metabolites (thiol adduct/trapping risk). Can also support reducing conditions in culture (note impacts on redox and metal-ion states).
Redox/reactivity assessment | detox/trapping	616-91-1	A105421	N-Acetyl-L-cysteine (NAC)	PharmPure™, USP, Ph.Eur.; ≥98.5%	Classic intracellular antioxidant/thiol donor; used for ROS/electrophilic stress controls and troubleshooting whether an inhibitor induces oxidative stress/reactive intermediates (also used in GSH depletion/compensation contexts).
Redox/reactivity assessment | thiol system	70-18-8	G105427	Glutathione (reduced) (GSH)	Moligand™; for cell culture; ≥98%	Key intracellular thiol buffer system; used for reactive metabolite trapping/electrophilicity risk assessment and oxidative-stress controls; also supports reducing environments in in vitro enzymology.
Redox/enzyme tool | NOX/flavoprotein inhibition	4673-26-1	D113448	Diphenyleneiodonium chloride (DPI)	Moligand™, ≥97%	Common flavoprotein inhibitor tool compound, often used to suppress NADPH oxidase (NOX)–related ROS production; useful as an “ROS dependency” control (limited selectivity—interpret cautiously).
In vitro enzymology/metabolism | cofactor	2646-71-1	N464348	β-NADPH tetrasodium salt hydrate (reduced coenzyme II, tetrasodium salt hydrate)	≥97% (HPLC)	NADPH: key cofactor for redox enzymes (CYPs/reductases/dehydrogenases) and metabolism experiments; essential in inhibitor–enzyme IC₅₀ assays, metabolic stability, and ROS-generation systems.

Note: The above are representative Aladdin products. For additional specifications, please refer to the product list at the end of the article or search the Aladdin website by product name/CAS.

Aladdin: https://www.aladdinsci.com/