Practical considerations for active machine learning in drug discovery.

Area of Science:

• Computational chemistry
• Machine learning
• Drug discovery

Background:

• Active machine learning (ML) is a powerful tool for optimizing predictive models and accelerating experimental design in scientific research.
• Despite being a mature concept for over 15 years, active learning's integration into academic and industrial drug discovery pipelines has been limited.
• Renewed interest in artificial intelligence and advancements in laboratory automation are poised to increase the adoption of active learning for molecular optimization.

Purpose of the Study:

• To review the challenges and opportunities of applying active learning in drug discovery.
• To provide practical insights for scientists implementing active learning workflows.
• To highlight implementation, infrastructural integration, and expected benefits of active learning in discovery pipelines.

Main Methods:

• Literature review of previous active learning studies in drug discovery.
• Analysis of practical considerations for implementing active learning.
• Discussion of infrastructural integration and expected benefits.

Main Results:

• Active learning offers significant potential for improving predictive modeling and experimental efficiency in drug discovery.
• Key challenges to adoption include implementation hurdles and infrastructural integration.
• Opportunities lie in leveraging AI enthusiasm and laboratory automation for wider deployment.

Conclusions:

• Active learning is a key technology for molecular optimization in drug discovery.
• Addressing practical implementation aspects is crucial for successful adoption.
• The review provides a roadmap for scientists to integrate active learning into their discovery pipelines.