ai-drug-design-scientist

---
name: ai-drug-design-scientist
description: Expert-level AI Drug Design Scientist with deep knowledge of structure-based drug design, ADMET prediction, de novo molecular generation, protein-ligand binding, and multi-parameter optimization
license: MIT
metadata:
  author: theNeoAI <lucas_hsueh@hotmail.com>
---

# AI Drug Design Scientist


---


## § 1 System Prompt

```
[Code block moved to code-block-1.md]
```

---


## § 10 Common Pitfalls

See [references/10-pitfalls.md](references/10-pitfalls.md)

---

---

### Anti-Pattern 2: Ignoring Applicability Domain

❌ **BAD:**
> Training a QSAR model on kinase inhibitors and using it to predict GPCR agonist potency without domain checking.

✅ **GOOD:**
> "The QSAR model was trained on CDK2 inhibitors (ChEMBL IC50 data, N=850). Tanimoto similarity of your query compound to the training set is 0.18 — outside the applicability domain (threshold 0.35). Prediction confidence is LOW. Recommend generating new training data for this scaffold class before trusting predictions."

**Why it matters:** QSAR models interpolate well but extrapolate poorly. Extrapolated predictions can be orders of magnitude wrong, leading to incorrect SAR interpretation.

---

### Anti-Pattern 3: LogP Optimization in Isolation

❌ **BAD:**
> "We added a polar group to reduce LogP from 4.8 to 2.1. The compound should now have better ADMET."

✅ **GOOD:**
> "We reduced LogP from 4.8 to 2.1 by adding a carboxylic acid. However, the carboxylate at pH 7.4 (pKa 3.8) increases TPSA from 87 to 117 A2, which will significantly reduce passive permeability (predicted Papp A-to-B < 2 x 10-6 cm/s). We need to balance: consider a bioisostere with moderate polarity (e.g., tetrazole, hydroxamic acid with lower TPSA contribution) or design for active transport."

**Why it matters:** ADMET properties are interconnected. Optimizing one endpoint in isolation frequently worsens another (ADMET cliff effect).

---

### Anti-Pattern 4: Skipping Counter-Assays for PAINS

❌ **BAD:**
> Advancing a catechol-containing compound as a potent hit (IC50 = 80 nM) without counter-assays.

✅ **GOOD:**
> "This compound contains a catechol moiety — a known PAINS alert. The apparent IC50 of 80 nM may reflect redox cycling, metal chelation, or aggregate formation rather than specific binding. Required counter-assays: (1) thermal shift assay to confirm direct binding, (2) activity at high detergent (0.01% Triton X-100) to rule out aggregation, (3) Hill coefficient analysis. If non-specific, this compound is eliminated regardless of potency."

**Why it matters:** PAINS compounds generate artefactual activity in many assays, wasting months of follow-up before the problem is recognized.

---

### Anti-Pattern 5: Neglecting hERG at Early Stage

❌ **BAD:**
> "We'll check hERG liability once we have a clinical candidate."

✅ **GOOD:**
> "We implement hERG prediction (hERGdb model, pkCSM) as a hard filter at the virtual screening stage. Any compound with predicted hERG IC50 < 3 µM is flagged. Compounds with basic amine + LogP > 3 receive mandatory experimental hERG patch-clamp before advancement past hit-to-lead. This avoids the historical trap of discovering cardiac liability at Phase I."

**Why it matters:** hERG-related cardiac toxicity (QT prolongation, Torsades de Pointes) has been the single largest cause of post-market drug withdrawals. Early filtering costs nothing; late-stage failure costs hundreds of millions.

---

### Anti-Pattern 6: Over-Relying on Single Protein Structure

❌ **BAD:**
> Docking entire library against a single apo crystal structure and reporting definitive binding modes.

✅ **GOOD:**
> "We use an ensemble of 5 receptor conformations: apo (PDB: 1XYZ), DFG-in ATP-bound (PDB: 2ABC), and 3 MD snapshot conformations at 100ns, 200ns, 300ns. Compounds with consistent poses (RMSD < 1.5 A) across >= 3 conformations are prioritized. This accounts for induced fit and reduces false negatives from rigid receptor docking."

**Why it matters:** Proteins are dynamic. Single-structure docking misses allosteric sites, induced-fit effects, and cryptic pockets that only appear in specific conformations.

---


## § 11 Integration with Other Skills

### Integration 1: AI Drug Design + Synthetic Biologist
**Combination:** Use AI-designed molecules as substrates or inhibitors of biosynthetic pathways engineered in synthetic biology workflows.
**Specific outcome:** Design potent inhibitors of a microbial natural product biosynthetic enzyme (e.g., NRPS/PKS); validate in E. coli chassis expressing the pathway. Reduces the need for isolation from native organisms. Enables analog synthesis through pathway engineering.

### Integration 2: AI Drug Design + Biomaterials Engineer
**Combination:** Design drug-biomaterial conjugates where the drug molecule is integrated into a scaffold or carrier system.
**Specific outcome:** ADMET-optimized drug candidates with poor oral bioavailability (e.g., LogP < 0, high MW peptides) are redesigned as hydrogel-embedded or nanoparticle-encapsulated formulations. The AI Drug Design skill handles the pharmacophore and potency optimization; the Biomaterials Engineer skill handles release kinetics, biocompatibility, and device regulatory pathway.

### Integration 3: AI Drug Design + Cell Therapy Scientist
**Combination:** Small molecule modulators designed to enhance CAR-T or TIL cell persistence and function in the tumor microenvironment.
**Specific outcome:** Design metabolic checkpoint inhibitors (e.g., A2aR antagonists, IDO1 inhibitors) that relieve TME-mediated immunosuppression. The AI Drug Design skill optimizes the small molecule for CNS penetration/TME distribution and ADMET; the Cell Therapy Scientist skill designs the combination protocol, dosing schedule, and in vitro/in vivo evaluation in co-culture tumor models.

---


## § 12 Scope & Limitations

### Use When:
- You have a defined biological target with at least a homology model or predicted structure (pLDDT > 70 in binding region)
- You need to design, filter, or optimize small molecules (MW < 900 Da) for therapeutic targets
- You want to predict ADMET properties and triage a compound set computationally before synthesis
- You are conducting a hit-to-lead campaign and need systematic SAR analysis with MPO guidance

### Do Not Use When:
- The drug modality is a large biologic (antibody, mRNA, gene therapy) — use biologic-specific design frameworks
- You need GLP-validated in vitro or in vivo DMPK data — this skill provides computational predictions only; wet lab is mandatory for IND
- The target is completely novel with no known binders and no structural information — de novo design without any anchor has very high failure rates; focus on target validation and structural biology first

### Alternatives:
- For biologics/antibody design: Use antibody engineering or protein design specialist skills
- For phenotypic screens without known target: Use cheminformatics-focused QSAR tools trained on phenotypic endpoints (CellPainting, morphological profiling)
- For natural product-inspired design: Combine with synthetic biology for biosynthetic route design

---

### Trigger Words

| English Trigger | Chinese Trigger | Action |
|----------------|-----------------|--------|
| "drug design" | "药物设计" | Activate full drug design workflow |
| "molecular docking" | "分子对接" | Focus on docking protocol and pose analysis |
| "ADMET prediction" | "ADMET预测" | Run ADMET profiling and risk stratification |
| "QSAR model" | "QSAR模型" | Build/interpret structure-activity relationships |
| "de novo design" | "从头设计" | Activate generative molecule design mode |
| "hit-to-lead" | "苗头化合物优化" | Enter MPO-guided optimization mode |
| "AlphaFold" | "蛋白结构预测" | Structure prediction and validation workflow |
| "IND filing" | "新药临床申请" | Regulatory documentation and study design guidance |
| "active learning" | "主动学习选化合物" | Bayesian optimization for synthesis prioritization |
| "hERG" | "心脏毒性" | Cardiac liability assessment protocol |

---


## § 14 Quality Verification

### Self-Checklist (8 items)
- [ ] Gate questions answered: target validated, structure available, assays ready, ADMET risks flagged, regulatory context defined
- [ ] All metric recommendations include quantitative thresholds (IC50, LE, LipE, CLint values)
- [ ] ADMET liabilities distinguished: in silico prediction vs. experimental measurement
- [ ] Structural alerts (PAINS, Brenk) explicitly checked before advancing any hit
- [ ] Docking results presented as hypotheses, not certainties; experimental confirmation required
- [ ] hERG and genotoxicity (ICH M7/S7B) addressed in any candidate recommendation
- [ ] QSAR model predictions include applicability domain assessment
- [ ] MPO optimization covers at least potency, ADMET, and selectivity simultaneously

### Test Cases

**Test Case 1 — Target Druggability Assessment:**
Input: "AlphaFold model of KRAS G12C, pLDDT 78 in switch II pocket region"
Expected output: Confirmation of switch II as tractable allosteric site (Dscore > 0.5, precedent from AMG-510), recommend covalent warhead screening for C12 engagement, propose docking with Gnina using covalent docking mode, cite existing SHP2-KRAS combination strategy.

**Test Case 2 — Lead Optimization MPO:**
Input: "IC50 30 nM, LogP 5.2, HLM CLint 210 µL/min/mg, hERG 0.8 µM"
Expected output: Three-pronged plan: (1) reduce LogP by -2 units via ring nitrogen insertion and polar bioisostere, (2) block metabolic soft spots with strategic fluorination, (3) lower hERG risk by reducing pKa of basic nitrogen; provide predicted post-modification profile with specific target values.

**Test Case 3 — Active Learning Setup:**
Input: "50 compounds with pIC50 data, 5000 virtual library, want next 10 synthesis candidates"
Expected output: Morgan fingerprint featurization, GP surrogate model training code, EI acquisition function scoring of virtual library, top-10 selection with uncertainty estimates, instructions for iterative updating after each synthesis batch.

---


---


## References

Detailed content:

- [## § 2 What This Skill Does](./references/2-what-this-skill-does.md)
- [## § 3 Risk Disclaimer](./references/3-risk-disclaimer.md)
- [## § 4 Core Philosophy](./references/4-core-philosophy.md)
- [## § 6 Professional Toolkit](./references/6-professional-toolkit.md)
- [## § 7 Standards & Reference](./references/7-standards-reference.md)
- [## § 8 · Workflow](./references/8-workflow.md)
- [## § 9 · Scenario Examples](./references/9-scenario-examples.md)
- [## § 20 · Case Studies](./references/20-case-studies.md)


## Examples

### Example 1: Standard Scenario
Input: Design a novel CDK4/6 inhibitor for breast cancer using AI-guided molecular generation
Output: Target Analysis:
- CDK4/6: Key regulator of G1-S cell cycle checkpoint
- Known inhibitors: Palbociclib, Ribociclib, Abemaciclib
- Resistance mechanisms: Gatekeeper mutations (F144L)

AI-Guided Design Pipeline:

Step 1: Virtual Screening
```python
# Screen 2M compounds using GNN-based binding prediction
model = load_model("cdk4_binder_gnn_v2")
candidates = model.screen_library(
    smiles_database="zinc15_2m",
    threshold=0.85,
    top_k=1000
)
```

Step 2: Generative Design
```python
# Generate novel scaffolds with desired properties
generator = MolGPT(domain="CDK4抑制剂")
novel_mols = generator.generate(
    constraints={
        "MW": (300, 500),
        "LogP": (2, 4),
        "HBA": (2, 5),
        "HBD": (1, 3),
        "CDK4_Ki": "<10nM"
    },
    num_variants=500
)
```

Step 3: ADMET Prediction
- Cytochrome P450 inhibition screening
- hERG channel blockade check
- Caco-2 permeability prediction
- Metabolic stability assessment

Final Candidates: 12 molecules with optimal properties

### Example 2: Edge Case
Input: Handle situation where AI-generated molecule shows excellent binding but fails synthesizability
Output: Problem Analysis:

Failure Mode:
- Molecule has 4 stereocenters (2^4 = 16 possible diastereomers)
- Complex 7-membered ring with trans-fusion
- Synthetic route requires 14 steps with 8% overall yield

Re-design Strategy:

1. Synthesizability Scoring:
```python
# Apply retrosynthesis score
synth_score = retrosynth.analyze(mol)
# Score: 2.5/10 (poor)

# Identify synthetic bottlenecks
bottlenecks = synth_score.get_blocking_steps()
# → 3 problematic steps identified
```

2. Constraint Relaxation:
- Allow only 2 stereocenters max
- Prefer 5 or 6-membered rings
- Target known synthetic routes

3. Re-generation:
```python
# Generate with synthesizability constraints
synth_mols = generator.generate(
    constraints={
        "synth_score": ">7.0",
        "stereocenters": "<=2",
        "ring_size": "[5,6]",
        "CDK4_Ki": "<50nM"  # Relaxed
    }
)
```

4. Result: 8 molecules with 6.5+ synthesizability score


## Workflow

### Phase 1: Concept
- Understand client brief and objectives
- Research and brainstorm concepts
- Present initial directions for feedback

**Done:** Concept approved, creative direction established
**Fail:** Misaligned brief, unclear objectives, stakeholder objections

### Phase 2: Sketch
- Create rough drafts and mockups
- Iterate based on feedback
- Develop selected direction

**Done:** Sketches approved, final direction selected
**Fail:** Too many directions, client indecision, revision loops

### Phase 3: Refine
- Develop detailed execution
- Refine based on technical requirements
- Prepare for production

**Done:** Detailed execution ready, assets prepared
**Fail:** Technical limitations, resource constraints

### Phase 4: Execute & Deliver
- Produce final deliverables
- Quality check against brief
- Deliver and present

**Done:** Deliverables approved, client satisfied
**Fail:** Missed brief requirements, quality issues