De Novo Drug Design
```wiki # De Novo Drug Design Server Configuration
This article details the server configuration optimized for *de novo* drug design computations. This setup is designed to support resource-intensive tasks such as molecular dynamics simulations, docking studies, and generative chemistry models. This guide is aimed at new users setting up servers for these workloads. Please review the System Administration guidelines before proceeding.
Overview
- De novo* drug design involves creating molecules from scratch, rather than modifying existing compounds. This requires significant computational power, particularly for accurate scoring and simulation. The server configuration outlined below focuses on maximizing performance for these tasks, balancing CPU, memory, and storage requirements. It's important to understand the specific needs of your chosen software (e.g. AutoDock Vina, GROMACS, RDKit) as requirements will vary.
- **Scalability:** Design the system with scalability in mind. Consider using a cluster of servers managed by a job scheduler for larger projects.
- **Data Management:** Implement a robust data management strategy to organize and store simulation outputs efficiently.
- **Security:** Regularly update software and apply security patches to protect against vulnerabilities.
- **Documentation:** Maintain detailed documentation of the system configuration and software installations. Documentation Standards apply.
- **Testing:** Thoroughly test the system after any changes to ensure stability and performance. Testing Procedures should be followed.
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Hardware Specifications
The following table details the recommended hardware components. Note that these are guidelines, and specific requirements may vary based on the scale of your projects.
| Component | Specification | Notes |
|---|---|---|
| CPU | Dual Intel Xeon Gold 6248R (24 cores/48 threads per CPU) | Higher core counts are generally beneficial for parallelizable tasks. |
| RAM | 512 GB DDR4 ECC Registered RAM | Sufficient RAM is crucial for handling large molecular structures and simulation data. |
| Storage (OS & Software) | 1 TB NVMe SSD | For fast boot times and software installation. |
| Storage (Data) | 20 TB RAID 6 HDD Array | Provides ample storage for datasets, simulation outputs, and backups. Consider Network File System for shared access. |
| GPU | NVIDIA A100 (80GB) x 2 | Accelerates molecular dynamics and machine learning tasks. CUDA support is essential. |
| Network | 100 Gbps Ethernet | Facilitates rapid data transfer from/to other servers or storage systems. See Network Configuration. |
| Power Supply | 2000W Redundant Power Supply | Ensures system stability and uptime. |
Software Stack
The software stack is designed to provide a robust and efficient environment for *de novo* drug design. We leverage open-source tools where possible to minimize licensing costs.
| Software | Version | Purpose |
|---|---|---|
| Operating System | CentOS 8 (or equivalent RHEL derivative) | Provides a stable and secure server environment. Refer to Operating System Installation. |
| Batch Scheduler | Slurm Workload Manager | Manages and schedules computational jobs efficiently. See Slurm Configuration. |
| Molecular Modeling Toolkit | Open Babel | For chemical file format conversion and manipulation. |
| Docking Software | AutoDock Vina 1.2.0 | Performs molecular docking to predict binding affinities. Requires AutoDock Tools. |
| Molecular Dynamics Software | GROMACS 2021.1 | Simulates the physical movements of atoms and molecules. Leverages GPU Acceleration. |
| Machine Learning Framework | TensorFlow 2.8.0 (with GPU support) | For developing and deploying generative chemistry models. See TensorFlow Setup. |
| Programming Languages | Python 3.9, C++, Bash | Essential for scripting, analysis, and software development. Python Packages are crucial. |
System Configuration Details
Beyond the core hardware and software, specific system configurations are necessary for optimal performance. These include file system settings, user account management, and security hardening.
| Configuration Item | Details | Importance |
|---|---|---|
| File System | XFS with inode64 enabled | Offers improved scalability and performance for large files. File System Management provides details. |
| User Accounts | Dedicated user accounts for each researcher with appropriate permissions. | Enhances security and accountability. Follow User Account Policies. |
| SSH Access | Key-based authentication only; password authentication disabled. | Improves security by preventing brute-force attacks. Check SSH Configuration. |
| Firewall | iptables or firewalld configured to allow only necessary ports. | Protects the server from unauthorized access. See Firewall Management. |
| Monitoring | Nagios or Prometheus for system monitoring and alerting. | Provides real-time insights into system health and performance. Refer to System Monitoring. |
| Backups | Regular backups of all critical data stored on a separate server. | Protects against data loss. See Backup Procedures. |
| Software Licenses | Proper management of software licenses to ensure compliance. | Avoid legal issues. License Management is essential. |
Further Considerations
Main Page Server Hardware Software Installation Network Configuration Operating System Installation Slurm Configuration AutoDock Tools GPU Acceleration TensorFlow Setup Python Packages File System Management User Account Policies SSH Configuration Firewall Management System Monitoring Backup Procedures License Management Documentation Standards Testing Procedures System Administration ```
Intel-Based Server Configurations
| Configuration | Specifications | Benchmark |
|---|---|---|
| Core i7-6700K/7700 Server | 64 GB DDR4, NVMe SSD 2 x 512 GB | CPU Benchmark: 8046 |
| Core i7-8700 Server | 64 GB DDR4, NVMe SSD 2x1 TB | CPU Benchmark: 13124 |
| Core i9-9900K Server | 128 GB DDR4, NVMe SSD 2 x 1 TB | CPU Benchmark: 49969 |
| Core i9-13900 Server (64GB) | 64 GB RAM, 2x2 TB NVMe SSD | |
| Core i9-13900 Server (128GB) | 128 GB RAM, 2x2 TB NVMe SSD | |
| Core i5-13500 Server (64GB) | 64 GB RAM, 2x500 GB NVMe SSD | |
| Core i5-13500 Server (128GB) | 128 GB RAM, 2x500 GB NVMe SSD | |
| Core i5-13500 Workstation | 64 GB DDR5 RAM, 2 NVMe SSD, NVIDIA RTX 4000 |
AMD-Based Server Configurations
| Configuration | Specifications | Benchmark |
|---|---|---|
| Ryzen 5 3600 Server | 64 GB RAM, 2x480 GB NVMe | CPU Benchmark: 17849 |
| Ryzen 7 7700 Server | 64 GB DDR5 RAM, 2x1 TB NVMe | CPU Benchmark: 35224 |
| Ryzen 9 5950X Server | 128 GB RAM, 2x4 TB NVMe | CPU Benchmark: 46045 |
| Ryzen 9 7950X Server | 128 GB DDR5 ECC, 2x2 TB NVMe | CPU Benchmark: 63561 |
| EPYC 7502P Server (128GB/1TB) | 128 GB RAM, 1 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (128GB/2TB) | 128 GB RAM, 2 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (128GB/4TB) | 128 GB RAM, 2x2 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (256GB/1TB) | 256 GB RAM, 1 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (256GB/4TB) | 256 GB RAM, 2x2 TB NVMe | CPU Benchmark: 48021 |
| EPYC 9454P Server | 256 GB RAM, 2x2 TB NVMe |
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⚠️ *Note: All benchmark scores are approximate and may vary based on configuration. Server availability subject to stock.* ⚠️