Our ATLAS technology merges AcTive® Learning with Automated Synthesis and Screening. This revolutionary combination leverages machine learning to accelerate the discovery of novel drugs. ATLAS allows us to uncover therapeutic targets and identify corresponding drug molecules, creating a chemical atlas of druggable space and fundamentally transforming the landscape of drug discovery
Kimia's generative chemistry is based on successive iterations of chemical design, high-throughput synthesis, chemical biology, and machine learning. This powerful synergy creates a map of the relationship between chemical structure and protein function down to single-atom resolution. This atlas is key to unlocking the power of machine learning in generative drug design
With ATLAS' precision chemistry, we can access billions of target-directed compounds on demand, generating massive amounts of chemical and biological data in a short timeframe. These datasets include proteomic and gene editing information to ensure appropriate engagement of intracellular pathways. Machine learning interprets these datasets, revealing trends that guide future chemical design and lead optimization
Our ATLAS technology merges AcTive® Learning with Automated Synthesis and Screening. This revolutionary combination leverages machine learning to accelerate the discovery of novel drugs. ATLAS allows us to uncover therapeutic targets and identify corresponding drug molecules, creating a chemical atlas of druggable space and fundamentally transforming the landscape of drug discovery
Kimia's generative chemistry is based on successive iterations of chemical design, high-throughput synthesis, chemical biology, and machine learning. This powerful synergy creates a map of the relationship between chemical structure and protein function down to single-atom resolution. This atlas is key to unlocking the power of machine learning in generative drug design
With ATLAS' precision chemistry, we can access billions of target-directed compounds on demand, generating massive amounts of chemical and biological data in a short timeframe. These datasets include proteomic and gene editing information to ensure appropriate engagement of intracellular pathways. Machine learning interprets these datasets, revealing trends that guide future chemical design and lead optimization
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