Accelerating AI Drug Discovery with Quantum Fingerprinting.

QuantumFP: Quantum Mechanical 3D Fingerprinting

Quantum mechanics can readily evaluate the effects of covalent bond formation utilizing automated approaches that can be integrated into dock scoring, AI/ML workflows, and applied toward goals such as warhead tuning. Transform 2D representations to 3D structures with ease, and generate quantum mechanical data for large machine learning models through our automated workflow. Feed ML models a higher volume of data with quantum accuracy!

QuantumFP Features


When it comes to accuracy, quantum-driven 3D characterization is the way to go. Quantum mechanics has proven to be more reliable in identifying correct low-energy conformers and provides a wider array of descriptors that molecular mechanics simply can't touch.


With just one click, generate 3D structures for up to 1,000,000 compounds. What's more? The quantum mechanical properties are included in the output for each one.


Budget-savvy? We've got you covered. QSimulate’s software platform is optimized for less expensive "spot instance" computing. Calculate vast quantities of data for significantly less than wet lab experiments.

QuantumFP Throughput & Cost

Property SetThroughput
(Compounds / Day)
Time / Compound
(vCPU hours)
Basic (Semiempirical)312,0000.0096
Standard (Semiempirical)288,0000.0105
Expert (DFT + Semiempirical)1,2008.8
  • Based on maximum of 50 conformers per molecule generated, QM fingerprint of lowest energy conformer output
  • Estimates based on cross-section of ChEMBL
  • Throughput is based on standard per-user AWS allocation. Can be increased to provide significantly greater bandwidth as required.
logo dark


Accelerating Drug Discovery with Quantum Fingerprinting