
RXNT has spent over 25 years building cloud-based healthcare software designed for ambulatory practices and medical organizations of all sizes and specialties. Our innovative, AI-powered, and data-backed software solutions help practices grow, improve clinical efficiency, and streamline business operations—whether you're a solo provider, large healthcare organization, or billing services company.
With over 60,000 medical professionals across all 50 U.S. states relying on RXNT, our fully-integrated, ONC-certified software system includes Electronic Health Records (EHR), Physician Practice Management (PPMS), Medical Billing and Revenue Cycle Management (RCM), E-Prescribing (eRx), Scheduling, Patient Portal, mobile applications, and more. Every product works seamlessly as one system or can be used standalone, giving you flexibility to choose what works best for your practice.
Our SaaS-based Full Suite software solution integrates every area of RXNT through a secure, centralized database, enabling real-time data flow across clinical and administrative functions.
Whether you're modernizing your medical practice or scaling your healthcare business, RXNT delivers all-in-one technology to help you succeed. So far, users have transmitted over 125 million prescriptions and processed more than $7 billion in insurance claims.
Built for usability and accessibility, RXNT’s cloud-based software is available 24/7 from any device and includes mobile apps for iOS and Android. Simple, transparent pricing means no hidden fees, and every plan includes free implementation & training periods, data migration, storage, software updates, and U.S.-based customer service.
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SciSure is a platform for managing laboratory operations end-to-end, built for scientific organizations. It brings together ELN, LIMS, and Health & Safety tools so teams can document experiments, track samples, manage chemical inventory, and maintain compliance workflows that are structured and audit-ready.
By replacing fragmented systems with a single governed platform, SciSure helps organizations improve reproducibility, gain clearer operational visibility, and scale lab operations with less risk.
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ESMC
ESMC marks the latest innovation in the ESM series of protein language models, advancing the understanding of representation learning in protein biology. By training on an enormous dataset of billions of evolutionary sequences, it effectively captures representations that provide insights into the mechanistic aspects of protein structure and function. Utilizing a transformer architecture, the model prioritizes sequences as its main input and is trained on a dataset that includes up to 6 billion proteins. ESMC is designed for a range of applications within protein science, including structure prediction, functional annotation, protein design, and the investigation of evolutionary relationships among proteins. Furthermore, it has the ability to generate new proteins from partial sequences, structures, or specific functional requirements, which allows researchers to explore novel possibilities in protein design and biological research. The model is readily accessible through the Biohub Platform, enabling users to interact with it via an API and the ESM Python package, which offers quickstart resources for installation, API key generation, and connection to the platform, thus ensuring a user-friendly experience. This ease of access not only promotes wider participation in protein research but also fosters collaborative efforts across the scientific community, ultimately driving further advancements in the field. With its capabilities, ESMC opens new doors for innovation and discovery in protein science.
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Evo 2
Evo 2 is an advanced genomic foundation model that excels in predicting and creating tasks associated with DNA, RNA, and proteins. Utilizing a sophisticated deep learning architecture, it models biological sequences with precision down to single-nucleotide accuracy, demonstrating remarkable scalability in both computational and memory resources as context length expands. The model has been trained on an impressive 40 billion parameters and can handle a context length of 1 megabase, analyzing an immense dataset of over 9 trillion nucleotides derived from diverse eukaryotic and prokaryotic genomes. This extensive training enables Evo 2 to perform zero-shot function predictions across a range of biological types, including DNA, RNA, and proteins, while also generating novel sequences that adhere to plausible genomic frameworks. Its robust capabilities have been highlighted in applications such as the design of efficient CRISPR systems and the identification of potentially disease-causing mutations in human genes. Additionally, Evo 2 is accessible to the public via Arc's GitHub repository and is integrated into the NVIDIA BioNeMo framework, which significantly enhances its availability to researchers and developers. This integration not only broadens the model's reach but also represents a pivotal advancement in the fields of genomic modeling and analysis, paving the way for future innovations in biotechnology.
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