This is like having a super-smart coding assistant for drug discovery: chemists describe what kind of medicine they want in code or constraints, and the AI proposes new molecules and lab routes to make them—far faster than humans could by hand.
Traditional drug discovery is slow and expensive because chemists must manually design, optimize, and synthesize candidate molecules. PostEra uses AI to automate large parts of molecular design and synthesis planning, reducing cycle times and R&D costs while exploring a much larger chemical space.
Combination of proprietary medicinal chemistry data, synthesis data, and specialized models; strong integration into medicinal chemist workflows; and partnerships with pharma/biotech that continuously improve the models.
Hybrid
Vector Search
High (Custom Models/Infra)
Model training cost and data curation/labeling for high-quality structure–activity and synthesis data; plus inference latency and cost for large-scale virtual screening and multi-objective optimization.
Early Adopters
Positions itself as a code- and constraint-driven molecular design and synthesis platform tightly coupled to medicinal chemistry workflows, with strong emphasis on generative design plus retrosynthesis and makeability—rather than only virtual screening or target prediction.