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.
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.
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.
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.
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.
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.
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.
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.
Disease 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.
One 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.
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