Accelerate Schrödinger Molecular Dynamics

Schrödinger MD Workstations & Servers

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Expertly Engineered

To meet the requirements for Molecular Dynamics GPU Computing or highly complex Free Energy Perturbation (FEP) calculations.

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Fully Customizable

Exxact offers a wide range of customizable options from workstations to clusters to meet your budget.

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Plug and Play

Every Exxact system ship with the latest version of software installed, built for optimal performance out of the box. Avoid the configuration and setup mess for frictionless deployment.

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Intel Xeon W Dual-GPUWorkstation

VWS-1690441
Starting at

$5,603.40

Highlights
CPU1x Intel Xeon W Processor
GPU2x NVIDIA RTX GPU: RTX 6000 Ada, A6000, A5000
MEMUp to 512GB DDR4 Memory
STO4x 3.5" Internal Drives
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AMD Threadripper PRO Quad GPUWorkstation

VWS-151920277
Starting at

$7,064.20

Highlights
CPU1x AMD Ryzen™ Threadripper™ PRO Processor
GPU4x NVIDIA RTX GPU: RTX 6000 Ada, A6000, A5000
MEMUp to 1TB DDR4 Memory
STO8x 3.5" and 2x 2.5" Internal Drive Bays

SCHRÖDINGER SOFTWARE STACK

AutoQSAR, Canvas, ConfGen, Core Hopping, CovDock, Desmond, e-Parmacophores, Epik, FEP+, Field-Based QSAR, Glide, Induced Fit, Jaguar, KNIME Extensions, LigPrep, MacroModel, Maestro, MOPAC2012, OPLS3, Phase, PLDB, Prime, PrimeX, Protein Preparation Wizard, QikProp, QM-Polarized Docking, QSite, Shape Screening, SiteMap, WaterMap

Build your ideal system

Need a bit of help? Contact our sales engineers directly.

Accelerating MD Simulations with NVIDIA GPUs

GPU computing has been widely used to accelerate compute-intensive simulations and push the boundaries of discovery. The multi-core platform of NVIDIA® Data Center GPU provides the computational power far outpacing that of the typical CPU. Scientists and developers can access supercomputer scale performance to run later systems, more system or longer simulation timeframes.

FEP

Advantages of Free Energy Perturbation Calculations

Recent advances in force fields and sampling algorithms, along with free energy calculations, can now yield meaningful comparisons with experimental binding affinities. The confluence of these advances is allowing in silico simulations to contribute to real-life drug discovery efforts by providing better synthesis decisions during lead optimization.