Why has insitro's machine learning model for target identification shown a 15% decrease in predictive accuracy over the past month?

Introduction

The recent 15% decrease in predictive accuracy of insitro's machine learning model for target identification is a critical issue that demands immediate attention. This decline could significantly impact the company's ability to identify promising drug targets efficiently, potentially affecting the entire drug discovery pipeline. I'll approach this problem systematically, focusing on identifying the root cause, validating hypotheses, and developing both short-term fixes and long-term solutions.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking there might have been a recent change in the data pipeline. Has there been any modification to the data sources or preprocessing steps in the last month?

Why it matters: Changes in data quality or preprocessing can significantly affect model performance. Expected answer: Yes, we updated our data preprocessing pipeline two weeks ago. Impact on approach: If confirmed, we'd focus on validating the new pipeline and comparing it with the previous version.

• Considering the complexity of target identification, I'm wondering about the diversity of the training data. Has there been any shift in the types or sources of biological data used to train the model recently?

Why it matters: Changes in data distribution can lead to model drift and decreased accuracy. Expected answer: We've incorporated a new set of proteomics data from a different lab. Impact on approach: This would lead us to investigate the quality and compatibility of the new data source.

• Given the nature of machine learning models, I'm curious about the model's architecture. Have there been any recent updates or changes to the model architecture or hyperparameters?

Why it matters: Even small changes in model architecture can have significant impacts on performance. Expected answer: No recent changes to the model architecture. Impact on approach: If confirmed, we'd shift focus to data quality and external factors affecting model performance.

• Thinking about the broader context, I'm wondering if there have been any changes in the definition or measurement of "predictive accuracy" in the past month?

Why it matters: Ensures we're comparing apples to apples and not dealing with a change in metric definition. Expected answer: No changes in the definition or measurement of predictive accuracy. Impact on approach: If confirmed, we can rule out measurement issues and focus on actual performance decline.