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QUEST™ / Quantum–AI Drug Discovery

Innovating the critical bottlenecks in drug development.

QUEST™ integrates molecular modelling, experimental evidence, artificial intelligence and quantum-derived representations to support better decisions from hit discovery through preclinical prediction.

Q-Dock Hit Discovery↓Supernatural AI Lead Optimization↓Virtual CRO Preclinical Prediction↓

01 / WHERE QUEST™ INTERVENES

QUEST™ addresses the decisive bottlenecks in drug development.

QUEST™ is QIC’s integrated Quantum–AI drug-discovery solution. Its three specialized modules support the development lifecycle from identifying experimentally credible hits to optimizing candidates and assessing preclinical ADMET and PK risk.

DRUG-DEVELOPMENT LIFECYCLEQUEST™ COVERAGE
01Target
02Hit Discovery
03Hit-to-Lead
04Lead Optimization
05Preclinical
06Clinical
MODULE 01Q-DockTarget → Hit DiscoveryIdentify credible hitsMODULE 02Supernatural AIHit-to-Lead → Lead OptimizationOptimize candidate profilesMODULE 03Virtual CROPreclinicalAssess development riskDownstream
development

Clinical development is shown as the downstream stage, not a current QUEST™ module.

01

Uncertainty in selecting viable hits

Conventional AI-based virtual screening assigns predictive scores from learned data patterns. A high score, however, does not necessarily indicate actual binding affinity, selectivity or a valid binding pose, and false-positive candidates may fail in experimental validation.

Q-Dock ↗02

Simultaneous optimization of competing drug properties

Strong binding affinity alone does not make a viable drug candidate. Affinity, selectivity, permeability, solubility, metabolic stability and toxicity must be optimized together, even though improving one property may compromise another.

Supernatural AI ↗03

Late identification of ADMET and PK liabilities

ADMET and pharmacokinetic issues are often identified only after substantial time and cost have been committed to preclinical studies, potentially leading to candidate termination and development delays.

Virtual CRO ↗
Across the lifecycle

Limits of data-only AI. Sparse or biased datasets, unfamiliar chemical space and limited representation of molecular physics can reduce prediction reliability across the lifecycle.

02 / QUEST™ · MODULE 01

HIT DISCOVERY

Q-Dock

Q-Dock models both the target binding environment and candidate ligands, then compares their three-dimensional and electronic interaction patterns to predict binding affinity and the most plausible binding pose.

MODEL TRAINING & DATA FOUNDATION
Chemical DB + Quantum Modeling

Build learning representations from molecular and interaction data

→
3D Image Match ML Model

Train the pattern-recognition and scoring model used in candidate matching

Q-Dock / STEP 01
Quest | QICTARGET BINDING SITEBinding pocketSpatial structureElectronicenvironment
Target binding-site analysis
Binding pocket
Spatial structure · Electronic environment
  • TARGET BINDING SITE
  • Binding pocket
  • Spatial structure
  • Electronic environment
STEP 01

Target binding-site analysis

Define the binding pocket and its spatial and electronic interaction environment.

03 / QUEST™ · MODULE 02

SCREENING & LEAD OPTIMIZATION

Supernatural AI

Supernatural AI identifies the molecular fragments that drive activity and uses them to iteratively design candidates with improved affinity, selectivity and drug-like properties.

Supernatural AI / STEP 01
Quest | QICFragment librariesAssay resultsVirtualfragmentationDEL screeningACTIVITY SIGNALSABCNoise filtering
Fragment libraries · Assay results · Virtual fragmentation · DEL screening data
Extract activity-related chemical signals
Candidate fragment seeds · Noise filtering
  • Fragment libraries
  • Assay results
  • Virtual fragmentation
  • DEL screening
  • ACTIVITY SIGNALS
  • Noise filtering
STEP 01

Extract activity-related chemical signals

Integrate fragment libraries, assay results, virtual fragmentation and DEL screening data to identify credible activity signals.

  • Candidate fragment seeds
  • Assay and DEL evidence
  • Noise filtering
IDENTIFYEVALUATEDESIGN

Each designed candidate is reassessed and refined until the target balance of activity and drug-like properties is achieved.

04 / QUEST™ · MODULE 03

PRECLINICAL PREDICTION

Virtual CRO

Virtual CRO translates molecular structure into ADMET and PK predictions, PK/PBPK analysis and concentration–time profiles, enabling earlier comparison and optimization of preclinical candidates.

Virtual CRO / STEP 01
Quest | QICDRUG STRUCTURE INPUTMolecular structureSMILES
Molecular structure or SMILES
Drug structure input
  • DRUG STRUCTURE INPUT
  • Molecular structure
  • SMILES
STEP 01

Drug structure input

Molecular structure or SMILES

05 / QUEST™ IN ACTIONREAL-WORLD DRUG DEVELOPMENT PROGRAM 01

ORAL PEPTIDE DRUG DISCOVERY

QUEST™ advances a proprietary α4β7 program from candidate discovery to oral drug development.

QIC applies QUEST™ to discover and optimize its proprietary α4β7-targeted peptide candidate, including binding affinity, target selectivity, drug-like properties, PK/PD and toxicity prediction. D&D Pharmatech applies and optimizes its ORALINK™ platform and conducts experimental efficacy evaluation to advance the asset toward an orally deliverable drug candidate.

TARGETIntegrin α4β7
INDICATIONInflammatory Bowel Disease
MODALITYOral Peptide Therapeutic
PROGRAM PERIODJul 2026 – Jun 2028
QIC · QUEST™ APPLICATIONONE ASSET · THREE SPECIALIZED MODULES
MODULE 01

Q-Dock

BINDING ASSESSMENTTarget siteHit priority

Models the α4β7 binding environment and evaluates target–candidate interactions.

  • Binding-site analysis
  • Affinity and binding-pose prediction
MODULE 02

Supernatural AI

CANDIDATE OPTIMIZATIONFragment designBalanced properties

Designs and optimizes peptide candidates for activity, selectivity and developability.

  • Candidate discovery and design
  • Multi-property optimization
MODULE 03

Virtual CRO

PRECLINICAL PREDICTIONADMET / PKDevelopment risk

Predicts PK/PD and toxicity to identify liabilities and guide candidate refinement.

  • PK/PD and exposure prediction
  • Toxicity and safety assessment
RELATED COVERAGE
Seoul Economic TV ↗BioTimes ↗Asia Business Daily · KR ↗Asia Business Daily · EN ↗

06 / QUEST™

Connect platform capability with real drug-development programs.

Explore QIC’s proprietary pipeline or discuss a discovery, optimization or preclinical prediction program.

View Pipeline →Discuss a Program →