🔬 Target & Discovery Pipeline
ML READY
Binding Affinity Cutoff (Kd)
50 nM
Neural ADMET Toxicity Strictness
High
Max SAScore (Synth Accessibility)
4.2
Ligand Core
H-Bond Donor/Acceptor
Hydrophobic Pocket Contact
Mutation Residue (G12D)
Click/drag canvas to rotate & probe residue forces
📉 High-Throughput ML Funnel Attrition
In-Silico Time: 14.8 seconds1. Virtual Pool
500,000
100% In-Silico
2. Pocket Docked
12,480
-97.5% Filtered
3. ΔG < -9 kcal
842
-93.2% Eliminated
4. Neural ADMET
64
-92.4% Tox Filtered
5. Validated Leads
6 Leads
Passed All Gates
🏆 Top In-Silico Lead Candidates
Select row to inspect binding geometry and pharmacological profile| Candidate ID | SMILES / Structure Core | Affinity Kd (nM) | Docking ΔG (kcal/mol) | Off-Target Tox Risk | SAScore | MHC Presentation | Status |
|---|
📑 Lead Drug Dossier: CAND-8942
Selectivity: 142x over Wild-Type14.2 nM
Target Affinity Kd
-11.4 kcal
Binding Energy ΔG
2.8 / 10
Synth Accessibility
Predicted Mechanism: Allosteric switch-II covalent entrapment targeting Asp12 neo-residue in KRAS oncogene.
ADMET Safety Profile: 0.04 hERG cardiac inhibition risk, zero Ames mutagenicity alert, high oral bio-availability.
Resistance Mutation Risk: Low cross-reactivity with HRAS/NRAS wild-types (99.4% oncogene specificity).
💡 Biochemical Note: KRAS was historically labeled "undruggable" for four decades due to its ultra-high picomolar affinity for GTP and lack of deep surface pockets. Machine learning generative algorithms discovered transient cryptic pockets accessible during switch-II oscillation.
⏱️ AI vs. Traditional Oncology R&D Benchmark
Evidence Grounded
Scientific Impact Summary: By replacing blind combinatorial wet-lab screening with transformer-based 3D structure predictions, generative molecular docking, and neural toxicity gates, researchers compress the initial 5-year lead optimization phase down to weeks while avoiding costly late-stage clinical trial attrition.