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
Develop Drug Targets
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
Candidate Drug Discovery
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
Prediction of Drug Crystal Form
SOLUTIONS PLATFORM CAREERS
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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
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