DynaPocket
QUANTUM-AI PIPELINE ACTIVE

PRECISION
ONCOLOGY

Targeted molecular therapy driven by advanced biomechanical analysis. 150 TP53 mutations analyzed via IBM Quantum hardware with ML-powered druggability scoring.

CELLULAR INTEGRITY
99.8%
THERMAL LOAD
0.040K
MUTATIONS ANALYZED
150
ACTIVE TRIALS
14
SCROLL
QUANTUM-AI PIPELINE

The Druggability Engine

A six-stage computational pipeline integrating quantum computing, machine learning, and molecular dynamics for precision oncology.

STEP 01

ESMFold2 Structure Prediction

High-confidence protein structure modeling with mean pLDDT 87.55 across 150 mutations.

87.55 pLDDT
STEP 02

IBM Quantum Conformational Sampling

Real quantum hardware execution on ibm_fez — 37.3 minutes for full conformational landscape mapping.

ibm_fez
STEP 03

ML Pathogenicity Prediction

Logistic Regression ensemble achieving AUROC 0.711 on classical features alone.

AUROC 0.711
STEP 04

Quantum-ML Hybrid Scoring

T_primary therapeutic score = 0.65S + 0.15A + 0.20F combining structural, affinity, and functional metrics.

T_primary
STEP 05

Drug Hypothesis Generation

Automated matching to clinical trials — Y220C → Rezatapopt (PYNNACLE Phase II: 33% ORR).

33% ORR
STEP 06

RNA-Risk Detection

S-F discordance flagging identifies splice-disrupting mutations requiring RT-PCR validation.

23 variants
01
02
03
04
05
06
ESMFold2 Structure Prediction
MUTATION ANALYZER

Search TP53 Mutations

Query 150 clinically-validated TP53 mutations with quantum-AI druggability scoring and automated drug hypothesis generation.

FILTER BY DRUGGABILITY:
ANALYTICS DASHBOARD

Clinical Intelligence

Comprehensive analytics across 150 TP53 mutations — from quantum conformational sampling to therapeutic prioritization.

150
TOTAL MUTATIONS
112
PATHOGENIC
87.55
AVG pLDDT
0.711
ML AUROC
23
RNA RISK
37.3m
QUANTUM TIME

TOP 10 DRUGGABLE MUTATIONS

T_PRIMARY SCORE
TP53_Y220C
0.7024
#105
TP53_R248Q
0.6017
#149
TP53_R282W
0.5875
#130
TP53_R248W
0.5279
#112
TP53_R273H
0.5276
#80
TP53_V157F
0.5214
#103
TP53_R249S
0.5182
#104
TP53_R273C
0.4895
#54
TP53_R158L
0.4855
#60
TP53_R175H
0.4795
#44

ML MODEL COMPARISON

AUROC
Logistic Regression
0.7110.690
CLASSICALQUANTUM
Random Forest
0.7010.583
CLASSICALQUANTUM
XGBoost
0.6610.617
CLASSICALQUANTUM

Note: Classical features outperform quantum features for pathogenicity prediction, suggesting quantum data captures orthogonal conformational information better suited for therapeutic scoring.

QUANTUM ENERGY LANDSCAPE

IBM QUANTUM (ibm_fez)
Y220CR248QR282WG245SP119S
FIDELITY →
QUBO ENERGY →
HIGH DRUGGABILITY
OTHER
15 mutations with quantum data

T-SCORE DISTRIBUTION

8
0.30-0.35
15
0.35-0.40
32
0.40-0.45
45
0.45-0.50
28
0.50-0.55
12
0.55-0.60
8
0.60-0.70
2
0.70+
END-TO-END PIPELINE

From Quantum to Clinic

A complete computational pipeline integrating quantum hardware, machine learning, and molecular dynamics for therapeutic discovery.

STEP 01

Structure Prediction

ESMFold2 generates high-confidence 3D structures for all 150 TP53 mutations. Mean pLDDT of 87.55 ensures reliable binding site identification.

ESMFold2pLDDT ScoringRMSD Analysis
150 mutations modeled
Mean pLDDT: 87.55
DBD residues 102-292 tracked
Tetramerization domain analysis
STEP 02

Molecular Docking

AutoDock Vina performs rigid docking against the p53 DNA-binding domain. Binding affinities range from -6.36 to -8.80 kcal/mol.

AutoDock VinaPDBQT ConversionAffinity Scoring
Best affinity: -8.80 kcal/mol (K132N)
RMSD structural deviation tracked
Binding pocket analysis
Docked pose validation
STEP 03

Quantum Sampling

IBM Quantum hardware (ibm_fez) executes QUBO-based conformational sampling. 500 shots per mutation capture energy landscapes.

IBM QuantumQUBO Optimizationibm_fez
Backend: ibm_fez
500 shots per mutation
QUBO energy: -0.81 to -0.19
Fidelity: 0.19 to 0.81
STEP 04

Feature Engineering

Classical and quantum features merged: affinity, RMSD, pLDDT, COSMIC counts, QUBO energy, fidelity, and conformational diversity.

Feature FusionNormalizationEntropy Calculation
11 features per mutation
Structural + functional scores
Quantum descriptors
Clinical anchor integration
STEP 05

ML Prediction

Logistic Regression achieves AUROC 0.711 on classical features. Quantum features show orthogonal information content.

Logistic RegressionRandom ForestXGBoost
Best model: LR (AUROC 0.711)
Train/test: 80/20 split
Stratified sampling
StandardScaler normalization
STEP 06

Therapeutic Scoring

T_primary = 0.65S + 0.15A + 0.20F combines structural, affinity, and functional scores. Y220C ranks #1 with 0.7024.

T_primary FormulaDrug MatchingRNA-Risk Detection
Y220C: T_primary = 0.7024
23 RNA-risk variants flagged
Automated drug hypothesis
COSMIC frequency weighting

Y220C: T_primary = 0.7024

The top-ranked druggable mutation with a validated clinical candidate: Rezatapopt (PC14586) — PYNNACLE Phase II showing 33% overall response rate and 43% response in ovarian cancer.

33% ORR
43% OVARIAN
PHASE II ACTIVE