Medicinal chemists had no way into binding simulations—that was computational chemistry's territory. AI gave them a browser and a button.
A global pharmaceutical company running drug discovery programs across oncology, ophthalmology, urology, women's health, and immunology, with multiple molecular modalities including small molecules, PROTACs, antibodies, and peptide drugs.
Binding affinity prediction simulations were exclusively handled by computational chemists, placing them entirely out of reach for medicinal chemists...
“Until now, simulations using protein-compound complex structures required for SBDD were performed exclusively by computational chemists. However, by utilizing the revolutionary AI model Boltz-2 NIM, we believed that medicinal chemists themselves could proceed with structure-based compound design and hypothesis verification.”
Global pharmaceutical company specializing in oncology, immunology, and gene therapy.
NVIDIA is a technology company that specializes in semiconductors, graphics processing units, and artificial intelligence for applications in data centers, gaming, and more.
Astellas Pharma's Drug compound screening is part of this use case:
Related implementations across industries and use cases
Physics simulations took weeks to screen libraries. Boltz-2 runs 1,000x faster, cutting this to hours with 2x precision.
Matching phages required screening trillions of options. Gen AI simulates interactions, cutting discovery from 10 years to 2 months.
Teams reactively managed trials across scattered systems. AI now integrates data to predict bottlenecks and recommend interventions.
Physics simulations took weeks to screen libraries. Boltz-2 runs 1,000x faster, cutting this to hours with 2x precision.
Matching phages required screening trillions of options. Gen AI simulates interactions, cutting discovery from 10 years to 2 months.
Generic AI mangled chemistry terms; manual translation took months. Glossary-tuned AI now earns enough trust to publish before human review.
A maze of disconnected systems obscured global trial visibility. Now, a digital twin guides AI to automate heavily regulated workflows.
On-premise systems, dispersed and brittle, bottlenecked every release. AI agents now run routine dev steps — hours cut to minutes.
A mistranslated word could derail global R&D projects. Now, researchers instantly refine technical papers & communicate seamlessly across languages.
Medicinal chemists had no way into binding simulations—that was computational chemistry's territory. AI gave them a browser and a button.
A global pharmaceutical company running drug discovery programs across oncology, ophthalmology, urology, women's health, and immunology, with multiple molecular modalities including small molecules, PROTACs, antibodies, and peptide drugs.
Binding affinity prediction simulations were exclusively handled by computational chemists, placing them entirely out of reach for medicinal chemists...
“Until now, simulations using protein-compound complex structures required for SBDD were performed exclusively by computational chemists. However, by utilizing the revolutionary AI model Boltz-2 NIM, we believed that medicinal chemists themselves could proceed with structure-based compound design and hypothesis verification.”
Global pharmaceutical company specializing in oncology, immunology, and gene therapy.
NVIDIA is a technology company that specializes in semiconductors, graphics processing units, and artificial intelligence for applications in data centers, gaming, and more.
Astellas Pharma's Drug compound screening is part of this use case:
Related implementations across industries and use cases
Physics simulations took weeks to screen libraries. Boltz-2 runs 1,000x faster, cutting this to hours with 2x precision.
Matching phages required screening trillions of options. Gen AI simulates interactions, cutting discovery from 10 years to 2 months.
Teams reactively managed trials across scattered systems. AI now integrates data to predict bottlenecks and recommend interventions.
Physics simulations took weeks to screen libraries. Boltz-2 runs 1,000x faster, cutting this to hours with 2x precision.
Matching phages required screening trillions of options. Gen AI simulates interactions, cutting discovery from 10 years to 2 months.
Generic AI mangled chemistry terms; manual translation took months. Glossary-tuned AI now earns enough trust to publish before human review.
A maze of disconnected systems obscured global trial visibility. Now, a digital twin guides AI to automate heavily regulated workflows.
On-premise systems, dispersed and brittle, bottlenecked every release. AI agents now run routine dev steps — hours cut to minutes.
A mistranslated word could derail global R&D projects. Now, researchers instantly refine technical papers & communicate seamlessly across languages.