Solid-State Chemistry Computation Services
XtalPi builds up a cross disciplinary platform combining computational chemistry and artificial intelligence technology, prominent for our abundant computation resources and excellent computational experts, to empower the pharmaceutical solid-state R&D process.
XtalCSP™ Crystal Structure Prediction (CSP)
Crystal structure prediction performs the global search of crystal structures of the target molecule and the other optional components in the corresponding searching space, aiming at the crystal structure of the thermodynamically stable crystalline form and the relative stability between stable forms. Our CSP services cover a variety of systems including polymorphs, salts, cocrystals, hydrates/solvates, etc.
Cross validation between predicted forms and experimental forms to determine the energy ranking of experimentally obtained crystals
Risk assessment of form conversion
Recommendation of experimental conditions to prepare novel crystalline forms
Property predictions (solubility, morphology, mechanical properties, etc.) of crystalline forms in early-stage drug discovery
High success rate
All crystalline forms obtained by crystallization experiments can be covered.
Shortened research timeline
CSP can be completed in 2-3 weeks for regular systems and 6-8 weeks for those complex systems.
CSP does not require APIs or experimental facilities, which effectively reduces the cost of research and development.
Solid-state virtual Screening Services
XtalPi independently developed a solid-state virtual screening platform. By computing the binding propensity between the target API and selected solvents, coformers, counter-ions and carriers, the platform solves problems such as the difficulty of selecting the optimal coformer among numerous options and the long experimental verification cycle for the physical stability of solid dispersion, thereby improving the efficiency and success rate of experimental screening.
Selection of counterions and solvents for salts
Selection of coformers and solvents for cocrystals
Selection of solvents to avoid solvate formation
Selection of carriers for solid dispersions
Shortened project schedules and accelerated pharmaceutical R&D processes
Decreased sample usage and experiments compared to shotgun method
More comprehensive and reliable screening results with less risk of empirical omissions
XtalPi uses multiple models to predict the crystal morphology of the given polymorph under different crystallization conditions, exploring the variability and controllability of morphology under factors such as solvent type, supersaturation, and temperature. By adjusting the crystallization conditions, the original morphology of target polymorph can be changed more purposefully to accelerate the development of crystallization processes.
Crystalline products with preferred morphology can be obtained under recommended crystallization conditions, which facilitates post-processing by avoiding difficulties in filtration or uneven particle size distribution after compression.
Guide crystallization experiments to obtain desired morphology
More robust and accurate algorithms compared to existing software/algorithms