The AI BioX™ Platform features two key components, AIPharmX™ and AIChemX™, designed to explore a broader chemical space more efficiently and cost-effectively. These modules significantly decrease the need for wet experiments, thus speeding up the drug discovery process.
Tackle undruggable targets (e.g., GPCRs, RNAs, and IDPs) with molecular dynamics and AI-enhance sampling methods
Rational design and optimization of ADC linker and payload empowered by physics-based modeling and machine learning
Design and fast synthesis of macrocycle peptides through computational approaches and parallel synthesis
Discover allosteric site of intrinsically disordered proteins
Artificial intelligence for every step of pharmaceutical research and development
AIPharmXTM Platform
With over 100 AI models that can be seamlessly interconnected via workflows, customized solutions are crafted based on customer requirements. Problem-oriented strategy recommendations inspire experts to develop innovative ideas for directed molecular design, high-throughput evaluation, and supplementary synthesis and testing stages.
AIChemXTM Platform
Leveraging the physical principles of quantum mechanics, molecular mechanics, and statistical mechanics, we developed UniX-Pose for binding mode prediction and UniX-FEP for affinity prediction. These tools aid in molecular design, evaluation, and optimization.
Novel Targets
Novel Molecules
Druggability Evaluation
TM Platform
Literature mining (biology, pathway and functions, etc.)Structural analysis
Crystal structures analysis
SAR/QSAR analysis
Binding mode analysis
Interactions analysis
Structural modeling to define and refine binding pockets
Cryptic/allosteric site discovery
Structure/Ligand-based screening using physics- and AI-driven docking
Rescoring and binding free energy calculation
Binding kinetics modeling
Functlonal group exploratlon and SAR analysls
High-accuracy affinity prediction and optimization
ADME/Tox predictive modeling
Web-lab validation
Once a lead compound has undergone extensive optimization and has demonstrated a desirable profile in terms of potency, safety, and pharmacokinetic properties, it may be selected as a Preclinical Candidate (PCC).
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