Drug discovery is often a challenging and resource-intensive process, particularly when it comes to molecular structure determination. Terra Quantum AG’s TQ Chemistry seeks to address these inefficiencies using advanced computational tools, blending physics-driven AI with high-performance computing to streamline key stages of drug development. ![[Pasted image 20241220162450.png]] ### Key Highlights 1. **Unparalleled Speed and Efficiency** TQ Chemistry boasts groundbreaking speed, with its TTConf tool providing up to 24 times faster conformer searches compared to industry benchmarks like CREST. This capability significantly reduces timelines while maintaining high accuracy, demonstrated across diverse datasets such as CD25 and Astex. 2. **Broad Applicability and Innovation** The platform supports a range of applications, from small molecule docking to protein-protein docking, leveraging tensor-train optimization for accurate and scalable solutions. It also features GPU acceleration, delivering up to 400x speed improvements in electronic structure calculations and preparing for quantum-ready infrastructure. 3. **Tailored Expertise and Collaborative Approach** Terra Quantum’s multidisciplinary team of specialists in quantum machine learning, simulation, and tensor networks collaborates closely with pharmaceutical partners. This ensures customized solutions, from targeted research stages to optimized workflows, enhancing lead molecule quality while keeping costs in check. --- ### So What? TQ Chemistry offers a transformative approach for pharmaceutical companies, providing a competitive edge in drug discovery by cutting time, cost, and complexity. For organizations seeking to innovate in therapeutic development, investing in TQ Chemistry could mean faster results, better resource utilization, and readiness for future advancements in quantum processing. The tools' versatility ensures applicability across a wide spectrum of molecular challenges, making it a forward-thinking choice in computational chemistry. [[Molecular Modelling x Accelerating Conformer Search]] [Ref Paper](https://chemrxiv.org/engage/chemrxiv/article-details/6756f02a7be152b1d06a8ca8)