跳到正文
cccskills
免费在 GitHub 公开

tooluniverse-drug-synergy

Drug-combination synergy analysis — quantify whether two drugs together are synergistic, additive, or antagonistic using the standard reference models (Bliss independence, HSA / highest single agent, Loewe additivity, ZIP, and the Chou-Talalay Combination Index). Use when you have measured single-drug and combination effects (inhibition/viability) and need a synergy score. Explains which model to use, what data each one needs, and how to read the score. NOT for looking up pre-computed synergy in a database (use the SYNERGxDB tool / cell-line-profiling skill).

中文简介正在准备中。当前显示原文说明。

实测评分 未实测

查看安装方法

包含的文件(3)

  • SKILL.md5.8 KB
  • scripts/synergy_reference.py2.2 KB
  • test_synergy_reference.py1.3 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Drug-Combination Synergy Analysis

Decide whether a two-drug combination does more than expected (synergy), exactly as expected (additivity), or less (antagonism) — and pick the right reference model for the data you have.

"Synergy" only means something relative to a null model of additivity, and the models define additivity differently — so the first decision is which model, driven by what data you measured.

Step 0 — Pick the model by the data you have

You measured…Use modelToolInput
Single effects of A, B, and A+B at one dose pairBlissDrugSynergy_calculate_blisseffect_a, effect_b, effect_combination (each a fraction 0–1)
Effects of A, B, A+B across several dose pointsHSADrugSynergy_calculate_hsaeffects_a, effects_b, effects_combo (arrays)
Single-agent dose-response curves + one combination pointLoeweDrugSynergy_calculate_loewedoses_a_single/effects_a_single, doses_b_single/effects_b_single, dose_a_combo, dose_b_combo, effect_combo
Single-agent dose-response + combo point, want Chou-Talalay CICombination IndexDrugSynergy_calculate_cisame as Loewe + assumption
A full dose × dose viability matrixZIPDrugSynergy_calculate_zipdoses_a, doses_b, viability_matrix (% , 0–100)

Effects must be on a consistent inhibition scale. Bliss/HSA/Loewe expect fractional inhibition 0–1 (0 = no effect, 1 = complete kill). If your data is % viability, convert: inhibition = 1 − viability/100. ZIP takes the viability matrix in % directly. Mixing scales is the most common error.

Step 1 — What each model's "additivity" means

ModelNull (additive) expectationBest when
Bliss independencedrugs act independently: E_exp = E_a + E_b − E_a·E_bdifferent mechanisms; quick single-point screen
HSA (highest single agent)combo should beat the better single agent: E_exp = max(E_a, E_b)conservative "does it beat monotherapy?" question
Loewe additivitya drug combined with itself = additive (dose equivalence)same/similar mechanism; needs dose-response
ZIPcombines Bliss + Loewe; potency shift of one drug's curve by the otherdose-matrix screens (the SynergyFinder default)
Chou-Talalay CICI<1 synergy, =1 additive, >1 antagonism (median-effect)classic isobologram-style analysis with dose-response

There is no single "correct" model — state which one you used. Bliss and Loewe genuinely disagree for some combinations (that's expected, not an error); reporting two models (e.g. Bliss + HSA, or Loewe + ZIP) is good practice.

Step 2 — Run it

# Bliss (single dose pair, fractional inhibition)
tu run DrugSynergy_calculate_bliss '{"operation":"calculate_bliss",
  "effect_a":0.4,"effect_b":0.3,"effect_combination":0.7}'
# -> expected 0.58, bliss_synergy_score 0.12, "Strong synergy"

scripts/synergy_reference.py computes the Bliss, HSA, and Loewe-style expected combination effects side-by-side from one dose pair, so you can see at a glance whether the models agree before running the full tools.

Step 3 — Interpret the score

For Bliss/HSA/Loewe/ZIP, the synergy score is (observed − expected) (often ×100):

Score (fractional, ×100 scale)Call
> +10synergy
−10 to +10additive (no meaningful interaction)
< −10antagonism

For Combination Index (Chou-Talalay): CI < 1 = synergy, CI = 1 additive, CI > 1 antagonism (note the opposite direction — lower is more synergistic).

  • A positive Bliss/HSA score means the combination exceeds the additive expectation at that point.
  • Synergy is often dose-dependent — a combination can be synergistic at one ratio and antagonistic at another; for a matrix, report the synergistic region, not one number.

Step 4 — Gotchas (state these)

  • Scale mismatch (% viability vs fractional inhibition) — convert first (Step 0).
  • Effects near 0 or 1 (ceiling). If both single agents already kill ~everything, the combo can't show synergy (no headroom) — Bliss/HSA saturate; interpret with care.
  • ZIP/Loewe/CI need real dose-response with ≥3 non-zero, measurable-effect dose points per drug, or the Hill fit fails (the tools say so).
  • Model disagreement is normal — don't shop for the model that gives "synergy"; pre-specify the model and report it.
  • A synergy score is not efficacy — a strongly synergistic combination can still be weak overall; report the absolute combination effect too.

Honest limitations

  • These are reference-model synergy scores, not statistical tests — for confidence, replicate and report variability across the dose matrix.
  • Synergy in vitro does not guarantee clinical benefit (PK/PD, toxicity, scheduling all matter).

Related skills

  • tooluniverse-dose-response — fit the single-agent IC50/EC50 curves that Loewe/CI/ZIP need.
  • tooluniverse-cell-line-profiling — look up pre-computed combination synergy (SYNERGxDB).
  • tooluniverse-drug-repurposing / tooluniverse-network-pharmacology — rationale for combinations.

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

Simplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for DeepSpeed/FSDP/Megatron/DDP. Automatic device placement, mixed precision (FP16/BF16/FP8). Interactive config, single launch command. HuggingFace ecosystem standard.

中文简介正在准备中。当前显示原文说明。

huang-sh/DeepScience42026年7月15日 更新

ADMET property prediction for drug candidates. Full pharmacokinetic panel (Caco-2, PPB, clearance, CYP), toxicity (hERG, AMES, DILI), drug-likeness (Lipinski, QED), using RDKit descriptors and TDC models.

中文简介正在准备中。当前显示原文说明。

huang-sh/DeepScience42026年7月15日 更新

Interpretable ADMET analysis with mechanistic reasoning. Maps liabilities to structural causes and biological pathways. Based on CoTox (Park 2025) and DrugR (Liu 2026).

中文简介正在准备中。当前显示原文说明。

huang-sh/DeepScience42026年7月15日 更新

aeon

免费

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

中文简介正在准备中。当前显示原文说明。

huang-sh/DeepScience42026年7月15日 更新

arboreto

免费

Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.

中文简介正在准备中。当前显示原文说明。

huang-sh/DeepScience42026年7月15日 更新

astropy

免费

Comprehensive Python library for astronomy and astrophysics. This skill should be used when working with astronomical data including celestial coordinates, physical units, FITS files, cosmological calculations, time systems, tables, world coordinate systems (WCS), and astronomical data analysis. Use when tasks involve coordinate transformations, unit conversions, FITS file manipulation, cosmological distance calculations, time scale conversions, or astronomical data processing.

中文简介正在准备中。当前显示原文说明。

huang-sh/DeepScience42026年7月15日 更新

查看 huang-sh 的全部技能

报告此技能的问题