Thursday, November 21, 2024

XtalPi Launches XMolGen & XFEP for Drug Discovery

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XtalPi announced the official launch of XFEP and XMolGen, two proprietary software products designed to accelerate and enhance the efficiency of drug discovery. Upon this launch, XtalPi will offer commercial licenses with flexible terms for industrial and academic scientists to use the software based on their research goals. With user-friendly interfaces, these platforms are seamlessly integrated within a shared suite to enable streamlined drug discovery workflows.

Scientists can generate diverse compound libraries using XMolGen’s generative and predictive AI modules, and subsequently assess the potency of these compounds through physics-based ligand binding affinity predictions with XFEP — unlocking faster pathways to discovering novel therapeutics by efficiently tapping into diverse, unexplored chemical spaces.

Despite the widespread application of computational chemistry in drug discovery, unresolved issues persist. Virtual library creation often yields compounds that are unrealistic and unsynthesizable. XMolGen addresses this by using AI and big data to generate libraries that explore real chemical space and ensure synthesizability. Additionally, the growing appreciation for physics-based calculations in ligand binding has increased the demand for power-efficient free energy perturbation (FEP) predictions and versatile applications across various ligands and proteins. XFEP meets this need with advanced computational capabilities for accurate and efficient FEP predictions across challenging proteins and ligands.

Also Read: XpertDox & Blue Fish Pediatrics Boost AI Medical Coding

XMolGen – AI and Big Data-based Molecular Generation

XMolGen is an AI-powered software that enables the design and screening of molecules with a focus on novel chemical space. XMolGen utilizes a generative chemistry module for novel molecular designs that can be associated to comprehensive commercial building block libraries to ensure the accessibility of the compounds. XMolGen also features a predictive AI module to evaluate drug-like properties and rapidly rank molecules based on docking scores, allowing for the prioritization of compounds for further drug development. XMolGen covers diverse application scenarios including de novo molecular generation, focused-library creation, and virtual screening.

XFEP – High Accuracy Affinity Prediction By Physics-based Free Energy Perturbation

XFEP is high-accuracy free energy perturbation calculation software that can accurately evaluate the ligand binding affinity to target proteins, powered by XtalPi’s proprietary force field platform that was developed based on the company’s large training sets and a cloud platform capable of large-scale dynamic calculations. Whether deployed locally or via the cloud, XFEP is coded to optimize GPU power and parallel computation processes to improve per-hardware utilization, significantly reducing the computational resources and time required for prediction calculations. Researchers can achieve comprehensive, accurate predictions across a wide range of ligands, including noncovalent, covalent, peptide, macrocyclic ligands, and PROTACs, with high performance.

Proven Workflow, Proven Results

XtalPi has validated the accuracy and effectiveness of these tools across numerous drug discovery projects, from hit identification to lead optimization. With the launch of the XMolGen and XFEP software, researchers can now access this proven, seamlessly integrated workflow — generating real compound libraries with XMolGen and assessing binding affinities with XFEP— directly from their own desktops, empowering them to drive faster, more efficient drug discovery efforts.

“We are proud to present XMolGen and XFEP, two innovative tools designed to accelerate drug discovery,” said Dr. Jian Ma, CEO of XtalPi. “Harnessing the power of AI and quantum physics, we aim to support researchers in achieving greater accuracy and efficiency, reflecting our commitment to innovation in the life sciences.”

Source: PRNewswire

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