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

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"	"Drug Design"	Activate full drug design workflow
"molecular docking"	"Molecular Docking"	Focus on docking protocol and pose analysis
"ADMET prediction"	"ADMET Prediction"	Run ADMET profiling and risk stratification
"QSAR model"	"QSAR Models"	Build/interpret structure-activity relationships
"de novo design"	"De Novo Design"	Activate generative molecule design mode
"hit-to-lead"	"Hit Compound Optimization"	Enter MPO-guided optimization mode
"AlphaFold"	"Protein Structure Prediction"	Structure prediction and validation workflow
"IND filing"	"New Drug Clinical Application"	Regulatory documentation and study design guidance
"active learning"	"Active Learning Compound Selection"	Bayesian optimization for synthesis prioritization
"hERG"	"Cardiotoxicity"	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:

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

# 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

# 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:

Synthesizability Scoring:

# 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

Constraint Relaxation:

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

Re-generation:

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

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

About this skill

§ 1 System Prompt

§ 10 Common Pitfalls

Anti-Pattern 2: Ignoring Applicability Domain

Anti-Pattern 3: LogP Optimization in Isolation

Anti-Pattern 4: Skipping Counter-Assays for PAINS

Anti-Pattern 5: Neglecting hERG at Early Stage

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

§ 11 Integration with Other Skills

Integration 1: AI Drug Design + Synthetic Biologist

Integration 2: AI Drug Design + Biomaterials Engineer

Integration 3: AI Drug Design + Cell Therapy Scientist

§ 12 Scope & Limitations

Use When:

Do Not Use When:

Alternatives:

Trigger Words

§ 14 Quality Verification

Self-Checklist (8 items)

Test Cases

References

Examples

Example 1: Standard Scenario

Example 2: Edge Case

Workflow

Phase 1: Concept

Phase 2: Sketch

Phase 3: Refine

Phase 4: Execute & Deliver