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name: 'protac-design-agent' description: 'AI-powered PROTAC (Proteolysis Targeting Chimera) design for targeted protein degradation, integrating ternary complex prediction, linker optimization, and ADMET modeling.' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:

• read_file
• run_shell_command

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PROTAC Design Agent

The PROTAC Design Agent provides AI-assisted design of Proteolysis Targeting Chimeras (PROTACs) for targeted protein degradation. It integrates machine learning for ternary complex prediction, linker design, E3 ligase selection, and ADMET optimization to accelerate degrader drug discovery for oncology and other therapeutic areas.

When to Use This Skill

• When designing PROTAC degraders for a target protein.
• For optimizing linker chemistry and length.
• To predict ternary complex formation and degradation efficiency.
• When selecting optimal E3 ligase (CRBN, VHL) for the target.
• For optimizing ADMET properties of degrader molecules.

Core Capabilities

1. Warhead Selection: Identify optimal target protein ligands.

2. E3 Ligase Selection: Choose CRBN, VHL, or other E3 recruiters.

3. Linker Design: Optimize linker length, chemistry, and rigidity.

4. Ternary Complex Prediction: Model POI-PROTAC-E3 formation.

5. Degradation Efficiency Modeling: Predict DC50 and Dmax.

6. ADMET Optimization: Balance potency with drug-like properties.

PROTAC Components

Component	Function	Optimization Target
Warhead	Binds target protein (POI)	Affinity, selectivity
E3 Ligand	Recruits E3 ubiquitin ligase	CRBN/VHL binding
Linker	Connects warhead to E3 ligand	Length, flexibility, solubility

E3 Ligase Options

E3 Ligase	Ligand	Tissue Expression	Advantages
CRBN	Thalidomide analogs	Ubiquitous	Well-characterized
VHL	VHL ligands	Ubiquitous	High selectivity
MDM2	Nutlin analogs	Variable	p53-independent
IAP	SMAC mimetics	High in cancer	Dual mechanism
DCAF15	Indisulam	Variable	Novel chemistry

Workflow

1. Input: Target protein structure/sequence, known ligands (optional).

2. Warhead Design: Generate/optimize POI binding moiety.

3. E3 Selection: Choose optimal E3 ligase for target/tissue.

4. Linker Library: Generate diverse linker options.

5. Ternary Complex Modeling: Predict complex formation.

6. Ranking: Score by predicted degradation and ADMET.

7. Output: Ranked PROTAC designs with synthesis routes.

Example Usage

User: "Design a PROTAC to degrade BRD4 using CRBN as the E3 ligase, optimizing for oral bioavailability."

Agent Action:

python3 Skills/Drug_Discovery/PROTAC_Design_Agent/design_protac.py \
    --target BRD4 \
    --target_structure pdb:3MXF \
    --warhead_smiles "JQ1_core_smiles" \
    --e3_ligase CRBN \
    --linker_library peg,alkyl,piperdine \
    --linker_length_range 4,12 \
    --optimize_oral true \
    --output protac_designs/

Linker Design Parameters

Parameter	Options	Consideration
Length	2-20 atoms	Ternary complex geometry
Chemistry	PEG, alkyl, piperazine, triazole	Solubility, stability
Rigidity	Flexible vs constrained	Entropic penalty
Attachment	Connectivity points	Exit vector matching
MW Contribution	Varies	Total MW impact

Output Components

Output	Description	Format
PROTAC Structures	Designed molecules	.sdf, SMILES
Ternary Models	POI-PROTAC-E3 complexes	.pdb
Predicted DC50	Degradation potency	.csv
Predicted Dmax	Maximum degradation	.csv
ADMET Predictions	Solubility, permeability, etc.	.csv
Synthesis Routes	Retrosynthetic analysis	.json
Ranking	Prioritized designs	.csv

Degradation Efficiency Metrics

Metric	Definition	Target
DC50	Concentration for 50% degradation	<100 nM
Dmax	Maximum degradation achieved	>90%
Kinetics	Time to half-degradation	<4 hours
Selectivity	Off-target degradation	Minimal
Hook Effect	High-dose attenuation	Minimal

AI/ML Components

Ternary Complex Prediction:

• AlphaFold-Multimer adaptation
• Geometric deep learning
• Molecular dynamics validation

Degradation Modeling:

• Quantitative degradation prediction
• Transfer learning from degrader databases
• Multi-task learning (DC50, Dmax, kinetics)

Linker Optimization:

• Generative models (VAE, diffusion)
• Reinforcement learning
• Multi-objective Bayesian optimization

ADMET Prediction:

• Property prediction models
• Chameleonicity assessment
• Oral bioavailability scoring

Clinical Pipeline Status (2026)

PROTAC	Target	Phase	E3 Ligase
ARV-471	ER	Phase 3, NDA filed	CRBN
ARV-110	AR	Phase 2	CRBN
BGB-16673	BTK	Phase 3	CRBN
NX-2127	BTK	Phase 2	CRBN
KT-474	IRAK4	Phase 2	CRBN

Design Considerations

Factor	PROTAC Challenge	Solution
High MW	Poor permeability	Chameleonicity
Low Solubility	Limited exposure	Solubilizing groups
Hook Effect	Reduced efficacy at high doses	Optimize binding balance
E3 Saturation	Competition with other PROTACs	Target expression

Prerequisites

• Python 3.10+
• RDKit, Open Babel
• AlphaFold2/3
• Molecular dynamics (GROMACS/OpenMM)
• PyTorch for ML models

Related Skills

• Molecular_Glue_Discovery_Agent - Glue degraders
• TPD_Ternary_Complex_Agent - Complex prediction
• Molecular_Docking_Agent - Docking analysis
• ADMET_Prediction_Agent - Property prediction

ADMET Optimization Strategies

Property	Challenge	Approach
Permeability	High MW limits	Intramolecular H-bonds
Solubility	Lipophilicity	Polar linker groups
Metabolic Stability	Linker metabolism	Stable chemistries
Clearance	High metabolism	Optimize logD

Special Considerations

1. Target Suitability: Not all proteins are degradable
2. E3 Expression: Check tissue-specific E3 levels
3. Ubiquitination Sites: Surface lysines needed
4. Resistance: Target mutations, E3 downregulation
5. Selectivity: Validate off-target degradation

Quality Control Metrics

QC Check	Threshold	Rationale
Ternary Complex Score	>0.7	Productive complex
Linker Strain	<5 kcal/mol	Favorable geometry
ADMET Score	>0.5	Drug-like properties
Synthetic Accessibility	<5	Feasible synthesis

Author

AI Group - Biomedical AI Platform

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