Drug Discovery AI
Generative AI for protein structure prediction, molecular generation, and binding-affinity scoring with BioNeMo. Accelerate hit-to-lead from months to weeks.
Product capabilities
Forge runs NVIDIA BioNeMo workflows to compress target discovery and lead optimization from months to weeks, with integrated experiment tracking for wet-lab handoff.
Generate novel drug-like molecules optimized for target binding, synthetic accessibility, and ADMET properties using BioNeMo generative models — screening billions of candidates in silico before synthesis.
Predict 3D structures for novel targets and variants with GPU-accelerated AlphaFold integration.
Link molecular targets to patient cohorts, genomics, and imaging phenotypes for precision discovery.
GPU-accelerated molecular docking and binding affinity prediction for library screening.
VPC or on-prem with IP protection, audit trails, and compound library versioning.
Traceable compound registry connects in-silico predictions to synthesis and assay results.
NVIDIA healthcare stack
GPU-accelerated generative AI framework for drug discovery — protein, small molecule, and antibody generation.
Structure prediction and molecular docking workflows with GPU acceleration for virtual screening at scale.
Performance metrics
Faster lead generation — compress hit-to-lead from months to weeks with GPU-accelerated screening
Molecules screened in silico before synthesis — maximize success rate for wet-lab validation
Protein structure prediction time with GPU-accelerated AlphaFold for novel targets
Virtual docking poses evaluated per day with NVIDIA GPU infrastructure
Experiment lineage tracking from in-silico prediction to wet-lab validation
Use cases
Link genomic alterations from patient cohorts (via Atlas and Helix) to protein structure predictions and druggability scores. Generate novel molecules targeting patient-derived mutations, then track from in-silico screening through synthesis and assay validation.
Optimize binding affinity, selectivity, and ADMET properties with generative models and virtual screening loops.
Generate novel scaffolds for challenging targets with no existing binders or structural analogs.
Screen billions of make-on-demand compounds against target structures using GPU-accelerated docking — prioritize synthesis candidates by predicted affinity and synthetic accessibility.
Generate and optimize antibody sequences for therapeutic targets with BioNeMo protein generation models.
How it works
Forge connects patient-derived targets to wet-lab experiments with GPU-accelerated generative AI at every stage.
Identify target from genomics, pathology, or literature; pull patient cohort data from Atlas
Predict 3D structure with GPU-accelerated AlphaFold; identify binding pockets
Generate billions of candidate molecules with BioNeMo optimized for target binding
GPU-accelerated docking and affinity prediction; filter by synthetic accessibility
Rank candidates by affinity, selectivity, ADMET, and synthesis feasibility
Export compound structures with full lineage tracking to synthesis and assay teams
Integration
Export compounds and predictions to Benchling, Dotmatics, or custom ELN systems
Ingest target structures from PDB, AlphaFold DB, or proprietary crystallography
Screen against Enamine REAL, WuXi, or custom make-on-demand libraries
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FAQ
Answers for drug discovery teams, computational chemists, and R&D leaders evaluating Forge.
Yes. Forge runs in VPC or on-prem via Citadel with full IP protection. You retain ownership of all target structures, generated molecules, and screening results.
BioNeMo MegaMolBART for small molecules, ESM and ProtGPT for proteins/antibodies, plus custom fine-tuned models on your compound library and assay data.
Forge combines GPU-accelerated docking (AutoDock, Glide) with learned scoring functions. Accuracy improves continuously as wet-lab results flow back into Atlas for model retraining.
Yes. We support Benchling, Dotmatics, and custom ELN/LIMS via API. Compound structures, predictions, and experiment lineage sync bi-directionally.
Get started
Bring a target discovery or hit-to-lead use case. We'll map the GPU architecture, structure prediction, and first measurable outcome.