Local or Global? The Big Question in AI Drug Discovery | Isomorphic Labs

2026-02-20 · Source: Weights & Biases · Field: Technology & Digital — Artificial Intelligence & Machine Learning, AI for Drug Discovery · Depth: Intermediate, quick

Summary

The application of machine learning to drug discovery is bifurcated into two primary approaches: local models and global models. Local models are trained on small datasets, typically thousands of data points, highly specific to an immediate problem. These models, often small Multi-Layer Perceptrons (MLPs), are applied to interpolate and extrapolate within a narrow, localized region of molecular space, leveraging initial wet lab experimental data to guide design. This approach is particularly useful when specific molecular regions have existing empirical data, allowing for focused optimization and prediction within that constrained domain.

Key takeaway

For AI Scientists or Machine Learning Engineers developing drug discovery solutions, understanding the distinction between local and global models is crucial. If your team has access to small, highly specific experimental data for a particular molecular space, you should prioritize building local models to efficiently interpolate and extrapolate within that constrained region, optimizing for immediate, targeted problems.

Key insights

Drug discovery ML models can be local (small data, specific problem) or global (large data, generalizable).

Principles

• Local models excel with limited, highly relevant data.
• Small MLPs are suitable for local data interpolation.

Method

Train a small MLP on thousands of problem-specific data points, such as wet lab results, to interpolate and extrapolate within a localized molecular region.

In practice

• Use local models for specific molecular design tasks.
• Apply MLPs with initial wet lab experimental data.

Topics

• Machine Learning in Drug Discovery
• Local Models
• Multi-Layer Perceptron
• Molecular Design
• Wet Lab Data

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Weights & Biases.