The global leader in cloud infrastructure, AWS provides the scalable foundation that powers Helix across compute, storage, and AI services. Our partnership leverages AWS’s GPU-accelerated environments and Bedrock platform to deliver reliable, high-performance pipelines for molecular modeling, data analysis, and generative AI.

The leader in GPU-accelerated AI infrastructure, NVIDIA’s NIM microservices deliver optimized inference for large models across diverse environments. Helix embeds these NIMs to power high-throughput modeling and molecular generation, offering tools for structure prediction, sequence generation, molecular design, and docking through AlphaFold2, ESM2, Evo2, GenMol, ProteinMPNN, RFdiffusion, MolMIM, and DiffDock.

Renowned for advancing physics-based simulation in drug discovery and materials design, Schrödinger’s software is a trusted standard in both industry and academia. Within Helix, we deploy Schrödinger’s modeling suite—Glide Docking, GridGen, LigPrep, Protein Prep, and Sitemap—to manage structure preparation, docking, and analysis with precision and scientific rigor.

With decades of leadership in chemical information and patent data, CAS provides scientists with a trusted foundation for discovery. Helix uses CAS’s Document, Markush, and Substance search services to deliver comprehensive access to literature, chemical structures, and patent information within a single, connected environment.

DISGENET is recognized for its curated knowledge linking genes, variants, and diseases to biomedical context. Through Helix, researchers can use DISGENET tools for enrichment analysis and exploration across genes, variants, chemicals, diseases, and publications, uncovering meaningful biological associations within their data.

Elsevier’s Reaxys provides authoritative chemical and reaction data, while SciBite’s CENTree and TERMite deliver advanced semantic text-mining capabilities. In Helix, these technologies work together to extract, normalize, and link scientific concepts across datasets, enriching chemical and textual data for more connected research insights.