For solid-state R&D of small molecule drugs, our solution integrates several state-of-the-art technologies for crystal structure prediction, crystal structure determination, virtual screening of salts and cocrystals, as well as an advanced solid-state R&D experimental platform. We are dedicated to creating the next-generation platform for solid-state R&D for small molecules by combining experimental screening and characterization with theoretical calculations and analyses, and providing novel solutions for the solid-state R&D. At XtalPi, we help our clients improve the quality and efficiency of solid-state R&D and reduce related risks and costs.
We offer predictions of the relative thermodynamic stabilities of different crystal structures through large-scale virtual crystal structure generation and high-precision energy calculation.
We offer services in experimental solid-state screening, crystallization process scale-up, and systematic characterization of crystal forms with desired properties, and recommend the ideal solid state to use in drug development based on systematic assessments.
Our solid-state research and development platform combines experimental data and theoretical analyses to reliably determine the crystal structures of solid forms.
Combining AI and computational chemistry, we developed a series of innovative methods to address challenging drug discovery and design tasks, including molecular structure generation, binding affinity prediction, and key drug property predictions. Based on these AI-empowered capabilities, we are working with industry partners in the discovery of small molecules and biologics to further expedite early-stage pharmaceutical research, improve research efficiency, reduce costs, and increase the overall success rate of innovative therapeutics R&D.
We combine AI, quantum mechanics, and cloud computing to develop a variety of intelligent digital drug discovery and design solutions. Coupled with state-of-the-art experimental technologies, our drug discovery solutions revolve around hit identification, lead generation, and lead optimization to yield high-quality pre-clinical candidate molecules.
Based on features extracted from protein sequence and structure, we have developed AI models to predict and optimize key characteristics of antibody drugs that affect their developability, and boost their success rate in the subsequent CMC process and clinical trials.
In combining theoretical computation, empirical data, and expert domain knowledge, we apply XtalPi’s AI-powered in silico drug R&D capabilities and industry expertise to develop better solutions for more efficient peptide discovery. Our active peptide generation and function prediction models can support the rational design and quick screening of various types of peptide drug candidates on a large scale.
The partnership will leverage XtalPi’s highly accurate physics-based models and machine learning models to develop potential novel chemical entities for a cancer target.
XtalPi and Sedec have recently reached an agreement to develop novel small-molecule therapeutics targeting STAT3 palmitoylation pathway for auto immune disorder diseases treatment.
XtalPi’s investment will allow the two companies to deepen their partnership in AI drug discovery against challenging novel targets