Binding Pocket & Druggability Analysis (Combination pocket drugability analysis)

From Flat Surface to Druggable Pocket. AI-Scored. MD-Sampled. Selectivity-Profiled.
Pocket Detection AI Druggability Scoring Selectivity Profiling

Many therapeutic targets lack obvious druggable pockets. We identify, score, and prioritize binding sites using AI classifiers and physics-based metrics, delivering actionable pocket maps for hit discovery.

Why Pocket Analysis Is the Critical First Gate

Structure-based drug design fails when the pocket is unknown or undruggable. Seed-stage biotechs lack computational chemistry teams to distinguish real pockets from false positives; pharma teams pursuing PPIs, allosteric modulators, or covalent inhibitors need precise pocket geometry to guide library design. Our platform combines AI druggability classifiers trained on thousands of co-crystal structures with MD-based pocket dynamics sampling, identifying both orthosteric and cryptic sites that survive experimental validation.

What Sets the Platform Apart

AI + Physics Hybrid

ML classifiers predict druggability from geometric descriptors. MD simulations sample pocket dynamics and transient openings.

Cryptic & Allosteric

Static structures miss cryptic pockets. MD ensemble analysis identifies transient sites invisible in single snapshots.

Selectivity-First

Every pocket is cross-referenced against the human structural proteome to flag off-target liability before synthesis.

Technology Suite

Contents

AI Pocket Detection & Druggability Scoring

Machine Learning Trained on Experimental Structures

AI Pocket Detection & Druggability Scoring

Key Features:

  • ML classifiers (Random Forest/XGBoost) trained on 500+ co-crystal structures score pocket druggability from volume, lipophilicity, and hydrogen-bond potential.
  • Deep learning models predict pocket ligandability probability for novel fold targets.
  • pLDDT-weighted confidence scores for AlphaFold-derived pocket geometries.

Ideal For — Target prioritization; orphan proteins; undruggable targets.

What We Offer:

You receive a ranked pocket list with druggability scores, not just a surface visualization. For hit identification teams, this directs library design toward the most ligandable site.

Cryptic & Allosteric Site Discovery

Dynamic Pockets Invisible to Static Methods

Cryptic & Allosteric Site Discovery

Key Features:

  • Microsecond-scale MD simulations identify transient pocket openings and allosteric communication pathways.
  • Ensemble pocket detection across 50–200 conformers captures dynamic pocket landscapes.
  • Cavity detection algorithms combined with MD trajectory analysis.

Ideal For — PPI disruptors; allosteric modulators; covalent inhibitor programs.

What We Offer:

For membrane proteins and PPI targets, cryptic pockets are often the only druggable option. Our MD-driven discovery finds binding sites that static models miss.

Selectivity Profiling & Target Prioritization

Off-Target Liability Before Synthesis

Selectivity Profiling & Target Prioritization

Key Features:

  • Pocket signature matching against human structural proteome identifies potential off-target liabilities.
  • Kinase selectivity profiling using binding site similarity matrices.
  • Target prioritization reports with druggability-vs-novelty trade-off analysis.

Ideal For — Multi-target programs; kinase selectivity; lead optimization.

What We Offer:

You receive a selectivity risk report before investing in synthesis. For pharma teams, this prevents late-stage attrition. For biotechs, this prioritizes targets with the best ligandability-to-competition ratio.

Platform Instrumentation

Core Instruments

Instrument / Software Capability
NVIDIA DGX A100 AI druggability model inference and large-scale MD
NVIDIA RTX A6000 Cluster Real-time pocket visualization and ensemble analysis
GROMACS/AMBER HPC Microsecond-scale MD for cryptic pocket sampling
FPocket / Ghecom Suite Geometric cavity detection and volume calculation
RDKit / OpenEye Toolkits Physicochemical descriptor calculation
Bruker AVANCE NEO 600 MHz NMR validation of predicted pocket ligandability

Standardized Workflow

Project Workflow

A milestone-driven execution system from target to prioritized pocket report.

01 Target Review Week 1
02 Pocket Detection Week 1–2
03 MD Sampling Week 2–3
04 Druggability Score Week 3
05 Selectivity Check Week 3–4

01 Target Review

1. Sequence and structure analysis
2. MSA and homology assessment
Deliverable: Target assessment report

02 Pocket Detection

  • 1. Cavity detection on static structure
  • 2. Initial pocket ranking by geometry
  • Deliverable: Preliminary pocket map

03 MD Sampling

  • 1. All-atom MD simulation
  • 2. Ensemble pocket detection
  • Deliverable: Dynamic pocket landscape

04 Druggability Score

  • 1. ML druggability scoring
  • 2. Cryptic site ranking
  • Deliverable: Prioritized pocket list

05 Selectivity Check

  • 1. Off-target liability scan
  • 2. Selectivity risk report
  • Deliverable: Final pocket report + SBDD recommendations

Sample Requirements

  • Target structure: PDB file, AlphaFold model, or sequence for de novo modeling
  • Known ligands: Any existing binders, cofactors, or reference compounds
  • Project scope: Target class, intended therapeutic area, and selectivity requirements
  • Prior data: Existing screening results or competitor intelligence (if available)

Standard Deliverables

  • Pocket prioritization report with druggability scores
  • Cryptic and allosteric site maps
  • MD ensemble analysis (50–200 conformers)
  • Selectivity profiling against human structural proteome
  • Final technical report with SBDD recommendations
  • Electronic data package (pocket trajectories, analysis scripts)

Frequently Asked Questions

Case Study

Case Study: Large-Scale GPCR MD Uncovers Hidden Allosteric Pockets & Lateral Gateways

Goal:

Map GPCR dynamic landscapes to expose concealed allosteric sites and membrane entry pathways invisible in static structures.

Key Data:

  • 556.5 μs dataset: 190 GPCR structures (33 subtypes) reveal apo receptors transition to open states in ~7.8 μs, while antagonists/NAMs slow this 7×.
  • Lipid insertion hotspots: Conserved TM1-TM7EC and TM3-TM4IC sites predict allosteric pockets; MD recovers 94% of experimentally observed lipid insertions.
  • Hidden pocket dynamics: Allosteric sites fully close within nanoseconds upon ligand removal, but lipid insertions trigger reopening—exposing transient druggable cavities and lateral entry channels (e.g., rhodopsin TM5-TM6EC).

Why it matters:

Static cryo-EM structures systematically conceal GPCR druggable sites. By treating lipid insertions as dynamic probes, this work reveals allosteric pockets and lateral gateways that enable structure-based design against previously unexplored cavities—compressing target validation by capturing biologically critical states traditional pipelines discard.

Lipid insertions in allosteric ligand-bound receptors

Figure 1. Lipid insertions in allosteric ligand-bound receptors by site location and polarity (top), exemplified by FFAR1 TM3-TM4-TM5IC pocket penetration (PDB: 5KW2, bottom). (Aranda-García D.; et al. 2025)

Reference

Aranda-García D, et al. Large scale investigation of GPCR molecular dynamics data uncovers allosteric sites and lateral gateways. Nat Commun. 2025 Feb 27;16(1):2020.

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From flat surface to druggable target — without building a computational chemistry team.
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