Drug Research and Development Solutions

icon-1Develop Drug Targets

Artificial intelligence learns a large number of medical literature and relevant data through Natural Language Processing (NLP), and analyzes the structural characteristics of a large number of drug targets and small molecule drugs independently.

  • Find the relationship between drugs and diseases
  • Find effective targets
  • Shorten the period of target discovery



icon-2Candidate Drug Discovery

AI technology uses big data and machine learning methods to automatically design millions of small molecular compounds related to specific targets based on existing drug development data, and screen compounds according to efficacy, selectivity, ADME and other conditions.

  • Process a lot of highly fragmented information
  • Develop virtual screening technology
  • Optimize the high-throughput screening process

icon-3Prediction of Drug Crystal Form

Artificial intelligence can improve the effect of crystallographic prediction to a great extent. It relies on the ability of deep learning and cognitive computing, processes a large number of clinical trial data, and can completely predict all possible crystallographic patterns of a small molecule drug.

  • Efficiently and dynamically configure drug crystal
  • Shorten the development cycle of crystal
  • Select the appropriate drug crystal form



icon-4 ADMET Prediction

Research technology was combined with computer simulation to study the interaction between drugs and biophysical and biochemical barrier factors in vivo. Prediction of ADMET is an important method in drug design and drug screening.

  • Effectively extract structural features
  • Deep neural network algorithm
  • Accelerate the early discovery and screening process

icon-5Repurpose Existing Drugs

Relying on AI's powerful natural language processing ability and deep learning ability, we can extract knowledge and new hypotheses that can promote drug research and development from the scattered and disordered mass information.

  • In-depth learning technology
  • Find new indications
  • Improve the curative effect



icon-6Design and Optimization of Clinical Trials

Artificial intelligence system can be used to guide clinical trials and data collection. With AI, different biomedical and healthcare data streams can be transformed into computer models that represent individual patients.

  • Optimize clinical trial research design
  • Eliminate unnecessary burden of clinical operation
  • Maximize potential to improve the possibility of clinical trial success

icon-7Patient Screening and Recruitment

Finding the right patients is the premise and basis of clinical trials. By using the in-depth research of disease data with artificial intelligence, pharmaceutical enterprises can more accurately mine target patients and quickly achieve patient recruitment.

  • Automatically match the trial results with the patient's situation
  • Improve the efficiency of accurate matching
  • Complete the trial recruitment in a short time




Through computer simulation, AI can predict the activity, safety and side effects of drugs. Supercomputers, AI and complex algorithms are used to simulate the pharmaceutical process to predict the effect of new drugs and reduce the cost of research and development.

  • Methods and experience in signal detection
  • Risk analysis and management
  • Find potential drug-related side effects earlier

Online Inquiry

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