The machine learning life cycle and the cloud: implications for drug discovery.

Area of Science:

• Drug Discovery
• Artificial Intelligence
• Machine Learning

Background:

• Artificial intelligence (AI) and machine learning (ML) are integral to modern drug discovery.
• Increasing data volumes and complex models like Deep Neural Networks (DNNs) present significant challenges in data management, software, and infrastructure.
• The need for continuous model retraining and deployment in production environments is critical for drug discovery.

Purpose of the Study:

• To explore how cloud computing can support the ML life cycle in drug discovery.
• To discuss the benefits of containerization, scientific workflows, and MLOps for reproducible ML.
• To address the challenges of ML on private, sensitive, and regulated data.

Main Methods:

• Leveraging cloud computing infrastructure for ML model development and deployment.
• Implementing containerization (e.g., Kubernetes) and scientific workflows for robust pipelines.
• Exploring Machine Learning Operations (MLOps) for managing the ML life cycle.
• Investigating federated learning for collaborative drug discovery on sensitive data.

Main Results:

• Cloud computing provides essential tools for integrating the ML life cycle into drug discovery.
• Containerization and workflows enable reproducible and resilient analysis pipelines.
• Cloud infrastructure offers scalable and efficient access to computational resources.
• Federated learning facilitates collaborative drug discovery with sensitive data.

Conclusions:

• Cloud computing is a powerful enabler for the ML life cycle in drug discovery.
• MLOps, containerization, and cloud infrastructure promote reproducible, resilient, and scalable ML.
• Cloud solutions can facilitate collaborative drug discovery while managing sensitive data.