Our physics-driven AI delivers precise, high-accuracy results—speeding up drug development with greater efficiency.
Thank you for your interest in TQchem—your partner in accelerating drug discovery through computational chemistry.
Our Enabling Technology
TQchem: Accelerating Drug Development Through Advanced Computational Chemistry
TQchem is our proprietary computational framework, offering a unique set of modelling capabilities for chemistry and computer-aided drug design, like conformer search, molecular, and protein-protein docking. It harnesses Terra Quantum's physics-driven tensor train optimization methods and GPU-accelerated simulation techniques for exceptional performance.
Key Features of TQchem
Depending on the project needs, TQchem offers a variety of molecular description methods with different cost-to-accuracy ratios, including force-fields, semiempirical and ab initio methods. For large-scale calculations with high accuracy, we provide GPU-accelerated DFT code with hybrid functionals.
Request a TQchem Exploration Session
Join our Beta to build, test, and deploy secure quantum solutions—no-code or SDK.
Accelerated Conformer Search
Small Molecule Docking to Proteins
Protein-Protein Docking
GPU-Accelerated Electronic Structure
Accelerate Discovery with Scalable Quantum Chemistry Tools
Explore a modular suite of high-performance quantum chemistry methods designed for accuracy, flexibility, and speed—tailored to meet the unique demands of your molecular modeling workflows.
Conformer Search Using Tensor Train Optimizer
TTConf (Powered by TetraBox)
Significant Speed-Up: Provides up to 24x faster results compared to state-of-the-art methods on datasets like CD25, BACE and Astex.
High Accuracy: Delivers precise conformers with fewer function evaluations.
Supports multiple backends for energy evaluation: Force Fields, Semiempirical Electronic Structure Methods, Ab Initio Electronic Structure Methods.
Benefits:
- Fast and accurate screening of numerous local minima.
- Generation of comprehensive conformer ensembles.
- Utilizes data-agnostic tensor train optimization.
* Prototyped in collaboration with Prof. Christoph Bannwarth & Christopher Żurek
Small Molecule Dock to Proteins
TTBind (Powered by TetraBox)
Optimized Binding Positions: Utilizes tensor train optimization to find optimal ligand binding positions.
Automatic Binding Site Detection: Identifies binding sites without prior knowledge.
Supports multiple backends for energy evaluation: Force Fields, Semiempirical Electronic Structure Methods.
Smart Optimization: Features intelligent internal coordinates to enhance optimization efficiency.
Protein-Protein Docking
TTConf (Powered by TetraBox)
Enhanced Precision: Achieves 40% better minima than the GSO algorithm in LightDock, with comparable computational cost.
Focused on rigid-body protein-protein docking for improved performance.
Utilizes data-agnostic tensor train optimization
Supports multiple backends for energy evaluation: Force fields, Semiempirical electronic structure methods
GPU-Accelerated Electronic Structure
Incredible Speed: Offers up to 400× speed-up compared to state-of-the-art CPU methods.
High Accuracy: Delivers precise energy calculations using ab initio methods.
Key Features
- CUDA-Enabled quantum chemistry simulations
- Advanced Calculations: Closed-shell Hartree-Fock and DFT calculations
- Efficiency: Features RI-J and SnK methods for studying large molecules
- Functional Variety: Includes LDA and GGA functionals provided by LibXC
* Benchmark details: Coulomb matrix calculation using RI method, C60 molecule with cc-pvdz basis and cc-pvdz-jkfit auxiliary basis. Results are for 1x NVidia V100 vs single CPU core of Intel Xeon Scalable 8173M. Tests were run on GCP environment.
TQchem Tools
Advanced quantum tools for simulating and optimizing molecular structures.
Single-Point Energy Calculations
- Custom-developed GPU-accelerated Ab initio HF and DFT methods
- Prediction of electronic properties of the molecule
- Supports multiple backends; force fields, semi-empirical and ab initio methods
Geometry Optimization
- Global and local optimization with both gradient-free tensor train and gradient-based methods
- Optimization with constraints and restraints
- Optimization using internal cooridinates
Internal Coordinates Scan
- Analysis of the energy landscapes w.r.t. change of an internal coordinate
- Manually refine conformers
- Select initial conformer for optimization
- Evaluate structure dependent properties
Dr. Roman Ellerbrock
Head of Applied Research, Chemistry
Tensor train techniques for conformer search offer a physics-driven approach, unlike many machine learning-based methods that rely on large datasets. Our method finds highly accurate conformers with significantly fewer function evaluations than competing optimization techniques, delivering substantial speed-ups and a data-independent solution.
Request Access for Non-Commercial Use
License Access for Academic and Research Projects
To support non-commercial projects and scientific research, we offer free access to selected features of TQchem. If you are an academic researcher or involved in non-commercial work, we invite you to apply for a non-commercial license key to explore TQchem’s advanced computational chemistry tools. Please reach out to our team to learn more and obtain your license.
Interested in Trying Out TQchem?
Join our Beta to build, test, and deploy secure quantum solutions—no-code or SDK.
Terra Quantum is committed to supporting research progress, offering free access to selected features of TQChem. If you'd like to experiment with TQchem for non-commercial projects, we invite you to apply for a non-commercial license key to explore TQchem’s advanced computational chemistry tools.
Design Principles
Academic Alignment
Developed in close collaboration with the academic community
Easy Integration
Features an API to allow easy integration to the existing pipelines
Experimentation
Features separate Python API and web interfaces to run experiments
Dive Deeper with Our Research Publications
Explore groundbreaking findings and real-world quantum applications.
Forecasting the Steam Mass Flow in a Power Plant Using the Parallel Hybrid Network
09/19/2024
Hybrid Quantum Cycle Generative Adversarial Network for Small Molecule Generation
07/16/2024
Quantum Physics-informed Neural Networks for Simulating Computational Fluid Dynamics in Complex Shapes
05/22/2024
Hybrid Quantum Image Classification and Federated Learning for Hepatic Steatosis Diagnosis
03/06/2024
Photovoltaic Power Forecasting Using Quantum Machine Learning
12/27/2023
Hybrid Quantum ResNet for Car Classification and Its Hyperparameter Optimization
09/29/2023
A Supervised Hybrid Quantum Machine Learning Solution to the Emergency Escape Routing Problem
07/28/2023
Quantum Algorithms Applied to Satellite Mission Planning for Earth Observation
06/19/2023
An exponentially growing family of universal quantum circuits
05/22/2023
Benchmarking simulated and physical quantum processing units using quantum and hybrid algorithms
05/22/2023
Parallel Hybrid Networks: an interplay between quantum and classical neural networks
05/22/2023
Start Experimenting with TQchem
Request Access Today for Your Non-Commercial Projects
Interested in Trying Out TQchem?
Join our Beta to build, test, and deploy secure quantum solutions—no-code or SDK.
Terra Quantum is committed to supporting research progress, offering free access to selected features of TQChem. If you'd like to experiment with TQchem for non-commercial projects, we invite you to apply for a non-commercial license key to explore TQchem’s advanced computational chemistry tools.
Ready to Accelerate Your Drug Discovery Process?
