Pharmaceutical computing spans molecular dynamics, docking, quantum chemistry, omics, document intelligence, trial analytics and manufacturing. A useful architecture joins GPU acceleration with large CPU/memory stages, governed datasets and reproducible evidence.
Discovery workload portfolio
Screening and model training are throughput-oriented; molecular dynamics may require tightly coupled GPU/CPU execution; free-energy or ensemble studies create many independent replicas; cheminformatics and cohort analysis may be CPU/memory-heavy. Classify jobs by fit and communication before creating scheduler partitions.
Platform choices
X1460 AxiForge Max concentrates four 96GB GPUs and 1.15TB RAM in one node for model training and accelerated simulation. X3460 AxiBastion offers twelve 96GB GPUs across three RDMA-connected nodes for departmental campaigns and concurrent teams. X3140 AxiLattice provides 384 CPU cores and 3.4TB RAM for CPU-led pipelines, ETL, statistics and shared workflow execution.
Molecular and AI software stack
GROMACS can partition work across CPUs and GPUs; validate the specific force field, system size and PME arrangement. BioNeMo provides frameworks for biomolecular generative and predictive models. Place these in signed containers with named datasets, structures, parameters and seeds. Use MLflow or equivalent metadata for experiments but keep authoritative scientific records in governed repositories.
From experiment to validated pipeline
Development allows rapid exploration; a validated process requires intended purpose, specified inputs, acceptance criteria, controlled dependencies, audit trail and change assessment. Treat models, feature code and reference databases as versioned components. Separate test evidence from production records and preserve the exact environment that produced a decision.
Confidentiality and collaboration
Compound structures, assay results and trial data require project-level access, controlled egress and partner-specific workspaces. Keep management interfaces isolated. Review export and sanctions requirements for high-performance components, models and technical data before cross-border access or shipment.
Sizing and acceptance
- Select representative MD systems, training jobs and CPU pipelines.
- Measure GPU occupancy, CPU preparation, RAM, scratch and fabric use.
- Test single-node efficiency before multi-node scaling.
- Define throughput per campaign and concurrent-user assumptions.
- Commission with scientific regression tests, recovery tests and documented environment manifests.
Primary technical references
- NVIDIA BioNeMo Framework documentation
- GROMACS heterogeneous parallelisation
- PyTorch distributed overview
- NVIDIA NCCL documentation
- MLflow Tracking documentation
References are provided for software architecture and implementation planning. Validate the versions, licences, support matrix and regulated-use requirements applicable to the final deployment.