晶泰科技

Imagine extracting 100+ compound structures and uncovering SAR insights from patent data in under an hour. With PatSight, XtalPi’s AI-powered chemical structure recognition tool, you can automatically capture key chemical structures and data (like cellular assay data) from up to 12 patents, delivered in editable CSV or SDF formats. Paired with our data management and analysis solution, you can instantly explore SAR and IP landscapes—saving hours and accelerating your path to new drug discovery. Check out our brochure down below! #patentanalysis #SARanalysis #PatSight #drugdiscovery

Check out our application note here: https://hubs.li/Q02ZqklB0 At XtalPi, we’re advancing DNA-Encoded Library (DEL) screening by integrating it with our XFEP platform to deliver fewer but higher-quality compounds while accelerating and improving hit discovery success rates. Our approach elevates DEL screening by: 1?? Prioritizing High-Potential Compounds: Leverage virtual screening to effectively narrow down enriched DEL libraries and reduce synthesis demands. 2?? Enhancing Accuracy: Incorporate docking scores as a computational validation layer, increasing confidence in hit identification. Learn more about XFEP: https://hubs.li/Q02ZqDQS0 #DrugDiscovery #DELScreening #ComputationalBiology #AIinPharma #XtalPi

Check out our new solid state white paper: https://hubs.li/Q02Z6zkW0 Polymorph screening is essential for drug formulation and development, but conventional methods are often time-consuming and resource-intensive. XtalPi's Crystallization Propensity Prediction Model uses hierarchical AI-driven predictions to improve success rates without relying on first-principles computation. By identifying key molecular and interaction features, the model ranks and recommends conditions to streamline and optimize screening. Key benefits include: ??Optimized Resource Utilization: Reduces manual work and experimental conditions, boosting efficiency and material use. ??Unbiased Experimental Design: Pre-trained on 140,000 simulations and fine-tuned with 7,000 experimental data points for reliable predictions. Integrated with our automation platform, we deliver efficient experiments, reduce manual effort, and ensure reliable results with enhanced crystallization propensity. Follow us on LinkedIn to stay up to date on our solid state white paper series! #crystallization #polymorphscreening #solidstateresearch #artificialintelligence

Request Your Free Trial Today: https://hubs.li/Q02YYPfl0 Free energy perturbation (FEP) is a powerful tool for binding affinity predictions, but its high computational costs and resource demands limit adoption. XtalPi's XFEP overcomes these challenges by using proprietary force fields and cloud-native infrastructure to screen thousands of compounds monthly, combining high computational throughput with superior accuracy in potency predictions. Here's what sets XFEP apart:? ?? Proven Track Record: Validated in 30+ drug discovery collaborations and 100+ targets. ?? Enhanced Computational Efficiency: Optimize resource usage, eliminate calculation bottlenecks, and integrate seamlessly into workflows, saving time and maximizing productivity. ?? Flexible Access: Full license or pay-as-you-go options with unlimited GPU usage. Follow us on LinkedIn to learn more! #freeenergyperturbation #FEP #drugdiscovery

Secure Your Free Trial: https://hubs.li/Q02Yf_cJ0 Small molecule drug discovery starts with the scaffold—the framework that shapes diversity, drug-like properties, and target engagement. Designing innovative scaffolds requires balancing novelty with feasibility to ensure compounds possess drug-like profiles and are synthetically achievable. Here's how XtalPi's XMolGen revolutionizes library scaffold design: ?? Explore Chemical Space: Create structurally diverse libraries with novel scaffolds, including sp3-enriched designs, to enhance solubility and selectivity for safer, more effective drug profiles. ?? Eliminate Chemistry Development Hurdles: Ensure synthesizable designs with feasible pathways using synthesizability assessment, saving time and resources. With XMolGen, researchers can perform de novo generation, virtual screening, retrosynthesis, and fragment-based designs, bridging ideation and discovery. Click here to learn more: https://hubs.li/Q02Yg5TN0 #LibraryDesign #ScaffoldInnovation #SyntheticFeasibility #DrugDiscovery

Imagine extracting 100+ compound structures and uncovering SAR insights from patent data in under an hour. With PatSight, XtalPi’s AI-powered chemical structure recognition tool, you can automatically capture key chemical structures and data (like cellular assay data) from up to 12 patents, delivered in editable CSV or SDF formats. Paired with our data management and analysis solution, you can instantly explore SAR and IP landscapes—saving hours and accelerating your path to new drug discovery. Check out our brochure down below! #patentanalysis #SARanalysis #PatSight #drugdiscovery

Join us at the Automated Synthesis Forum on November 11, where Dr. Sarah Trice will present "XtalPi’s Path to Predictive Chemistry." She’ll showcase how our automation-driven platform integrates software, hardware, and advanced algorithms to generate reliable reaction data, accelerating data-driven decisions in drug discovery and enabling faster paths to novel compounds. Make sure to stop by Booth 25 to meet our team! #ASF2024 #automation #automatedsynthesisforum #predictivechemistry

Read Here: https://hubs.li/Q02XqBrM0 KIF18A is critical in cell division, and inhibiting it presents a promising approach for cancers with chromosomal instability (CIN) or whole-genome duplication (WGD), such as ovarian and breast cancers. However, challenges like the shallow allosteric binding site and limited structural data make designing potent and safer inhibitors difficult. Our Approach: We utilized XPose, our proprietary simulation model, to gain structural insights and guide the virtual screening of AI-generated compounds. With scaffold hopping, we designed novel compounds targeting key pharmacophores. Key Outcomes: ?? XPose and XFEP enabled rational drug design and affinity prediction, streamlining preclinical candidate nomination. ?? Advanced from Lead-ID to the preclinical non-GLP tox study stage, synthesizing just 120 compounds. ?? Nominated XTN187 as a potential best-in-class oral agent with improved safety and efficacy in CDX models at comparable dosages to Sovilnesib. #drugdiscovery #cancertreatment #preclinicalcandidate

At XtalPi, we’re redefining drug discovery with our advanced software and cutting-edge lab automation platform. Our software enables novel compound design and exploration of untapped chemical space, while our lab automation transforms these creative ideas into real-world chemistry data with unmatched speed and reliability. Key features include: ?? Scalable Data Production: Automates up to 10,000 reactions weekly, covering 80% of common med chem reactions and generating 30,000+ data points monthly. ?? Integrated Chemistry Execution: Combines advanced software, intelligent algorithms, and modular workstations to optimize workflows and boost discovery efficiency. ?? High Reproducibility: Delivers consistent, quality results through optimized reaction conditions. Learn More: https://hubs.li/Q02X1F290 #AutomationInnovation #LabAutomation #AI #Robotics

?? Celebrating XtalPi’s and the Community’s Success in Crystal Structure Prediction! Since XtalPi's founding, one of our core missions has been to develop cutting-edge computational tools that tackle complex challenges in drug and materials discovery. Crystal Structure Prediction (CSP) is essential in fields like pharmaceuticals and materials science, aiming to accurately predict thermodynamically stable crystal structures. Our team is honored to participate in the 7th CSP Blind Test, which is a unique opportunity to collaborate with industry and academic peers, push scientific boundaries, and validate our efforts. Learn more about the Blind Test from CCDC - The Cambridge Crystallographic Data Centre: https://hubs.li/Q02X0RyX0 Key Takeaways: ?? Participants were challenged with 7 systems to predict experimental polymorphs and their relative stabilities using ab initio methods. ?? XtalPi (Group 10) was one of the only two first-tier performers (Group 10 and Group 20) among 28 participating groups. ?? Our CSP platform successfully predicted structures for 6 out of 7 compounds, identifying a polytypic structure for molecule XXIX within the RMSD_30 criteria. ?? The success of Groups 10 and 20 showcases the potential of current CSP methods, inspiring ongoing advancements in predicting molecular crystal structures. ?? The test results are documented in two articles, covering structure generation and the ranking of predicted crystal structure by stability, benchmarking current CSP techniques. Results of the 7th CSP Blind Test: https://hubs.li/Q02X0KZn0 Congratulations again to the XtalPi team and those who contributed to this milestone in advancing materials science research! #crystalstructureprediction #AI #materialscience #CCDC #7thCSPBlindTest