Explore Mysteries of AI and Quantum

Small Molecule Drug Design

Our graph network architecture, combined with quantum information technology, has significantly advanced the screening of small molecule drugs for anti-cancer and anti-neuro-inflammatory diseases. Innovative algorithms have improved active compound screening accuracy and valence bond analysis. By integrating quantum mechanics with AI, we've pioneered new methods for drug molecule design and property prediction, promoting biopharmaceutical industry intelligence

MOLECULAR FRAGMENTATION ANALYSIS INTEGRATED TECHNOLOGY

GENERATIVE MODELS (MOLECULAR DESIGN)

AI-based drug molecule generation models are changing drug design. Through algorithms such as GANs and VAEs, new drug molecules are quickly generated, efficacy and toxicity are predicted, chemical structures are optimized, and safety and effectiveness are improved. AI shortens the drug discovery cycle, reduces costs and increases success rates, and may provide innovative structures and mechanisms to help treat complex diseases. In terms of personalized medicine, AI can customize personalized drugs based on the patient's condition and genetic data to achieve precision treatment

RNA LARGE MODEL ANALYSIS TECHNOLOGY

DUAL-TARGET DRUG DESIGN PLATFORM

AutoSARM (SAR) TECHNOLOGY

New material design

Manual feature selection and simple models have difficulty processing high-dimensional, sparse single-cell RNA data, resulting in inaccurate classification and difficulty in identifying new cell types. To this end, we provide a corresponding solution

AUTOMATIC FEATURE EXTRACTION

Automatically extract important features from data through advanced algorithms, avoiding manual operations, greatly reducing human errors and improving analysis efficiency and accuracy

DEEP LEARNING

Capture complex patterns in data and improve analysis accuracy. Support deeper research

EFFICIENT CLASSIFICATION AND RECOGONATION

Significantly improve the accuracy of cell type classification and identify new cell types, accelerating the development of new drugs and the formulation of personalized treatment plans

Medical imaging

Our deep learning algorithms, especially convolutional neural networks (CNNs), excel in processing and analyzing large-scale medical image data. Through training, AI systems can identify complex features and lesions in medical images, such as tumors, inflammation, vascular abnormalities, etc. This automated image recognition capability greatly reduces the burden on doctors, allowing them to handle more cases in a shorter period of time

Our quantum mechanics first-principles calculation method has revolutionized R&D for new batteries and energy materials. Innovative algorithms boost lithium-sulfur battery energy storage by over 80% and reduce thermoelectric device design costs. We support new material design and development in electrocatalysis, photocatalysis, porous adsorption, and eco-friendly materials, driving intelligent transformation in the energy and materials industry.

The application of AI in health big data analysis covers disease prediction, personalized treatment plans, public health monitoring, etc. Through machine learning and deep learning algorithms, AI can analyze multi-source data such as electronic health records, genomic data, wearable device data, and medical images to identify potential health risks and disease patterns. For example, AI can analyze medical record data to predict the risk of chronic diseases such as diabetes and heart disease, and take preventive measures in advance.

In public health, AI can timely detect outbreaks and epidemic trends of infectious diseases through real-time analysis and monitoring of large-scale population data, and assist public health departments in formulating prevention and control strategies. For example, during the epidemic, AI can analyze social media and health report data, quickly track the virus transmission path, and evaluate the effectiveness of prevention and control measures. AI can also be used to optimize medical resources. By analyzing hospital operation data, predicting patient needs for medical treatment, optimizing doctor scheduling and bed allocation, and improving the efficiency and quality of medical services.

Health Big Data