Develop Drug TargetsArtificial 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.
Candidate Drug DiscoveryAI 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.
Prediction of Drug Crystal FormArtificial 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.
ADMET PredictionResearch 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.
Repurpose Existing DrugsRelying 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.
Design and Optimization of Clinical TrialsArtificial 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.
Patient Screening and RecruitmentFinding 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.
PharmacovigilanceThrough 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.
Biomarker DiscoveryDisease biomarker is a molecule that indicates changes in the physiology of a cell under diseased state and hence can be used as a diagnostic tool, therapy guidance and prognosis monitoring of diseases.
Drug Formulation DevelopmentOne of the main difficulties in formulation prediction is the small dataset with imbalanced input space resulting in overfitting and poor generalizations, AI-driven formulation platform is to enable targeted, smart novel drug candidates.
Solutions