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Crystal structure prediction (CSP) is a critical step in the development of drugs and organic electronic materials, where small energy differences between polymorphs can determine key properties such as solubility or conductivity. Traditionally, DFT-based methods with dispersion correction offer high accuracy, but their computational cost makes them unfeasible for high-throughput screening. In this context, FastCSP emerges as an open-source workflow that employs a single pre-trained universal interatomic potential (UMA) without the need for system-specific adjustments or additional DFT calculations. FastCSP integrates conformer generation, geometric optimization, free energy evaluation, and energy corrections, achieving 100% reliability in 28 semi-rigid and 10 flexible molecules, with all known polymorphs classified within 9 kJ/mol of the global minimum. This advance eliminates dependence on classical force fields and drastically reduces computation time, democratizing access to CSP for both pharmaceutical screening and advanced materials.
FastCSP's ability to reproduce DFT results with high fidelity without resorting to costly simulations opens new possibilities in industry. Companies like Q2BSTUDIO, specialized in artificial intelligence for businesses, can leverage this type of universal model to build custom software solutions that integrate structure prediction directly into materials discovery pipelines. The combination of AI agents trained with chemical data and AWS and Azure cloud services allows scaling these workflows to thousands of candidates in hours, rather than weeks. Additionally, cybersecurity and access control are crucial when handling intellectual property data of new compounds, an area where Q2BSTUDIO also offers protection and cybersecurity tools. On the analysis side, CSP results can be visualized using Power BI or business intelligence services to make quick decisions about which polymorphs to synthesize, thus integrating artificial intelligence with data analytics.
Conformational polymorph prediction, as in the case of the ROY compound, particularly benefits from the automated energy corrections introduced by FastCSP. This approach not only replaces classical force fields in early stages but also allows retraining the model with new data, generating tailored applications for each chemical family. Q2BSTUDIO, with its expertise in custom software development, can implement work environments that automate structure generation, optimization, and ranking, all orchestrated by AI agents that decide which candidates deserve deeper analysis. The synergy between these universal models and software customization is key for CSP to become a routine tool in academic and industrial laboratories, reducing R&D time and accelerating the arrival of new materials to market.
