Is Novelty Predictable?

Area of Science:

• Computational chemistry and structural biology
• Artificial intelligence in scientific discovery
• Molecular design and engineering

Background:

• Machine learning (ML) accelerates scientific design, particularly for molecules, materials, and proteins.
• ML applications span drug development, environmental remediation, and carbon capture.
• A core challenge is balancing exploration for novelty with risk management in ML models.

Purpose of the Study:

• To address the challenge of achieving novel property values in ML-based design.
• To explore strategies for balancing extrapolation and risk control in ML models.
• To focus on protein design while offering broader applicability to ML-based design.

Main Methods:

• Conceptual analysis of ML model extrapolation and risk.
• Framework development for controlled exploration in generative models.
• Case study focusing on protein property optimization.

Main Results:

• Identified the critical trade-off between model extrapolation and design failure.
• Proposed a balanced approach to ML-driven design for novelty and reliability.
• Demonstrated applicability to protein design challenges.

Conclusions:

• Striking a balance between trusting ML models and controlling extrapolation is crucial for successful scientific design.
• This approach enables the discovery of novel proteins with desired properties.
• The principles discussed are broadly applicable to various ML-based design fields.