What factors are contributing to the decreased accuracy of Schrödinger's FEP+ free energy calculations compared to last year's benchmark results?

Introduction

The decreased accuracy of Schrödinger's FEP+ free energy calculations compared to last year's benchmark results is a critical issue that requires immediate attention. This analysis will systematically identify, validate, and address the root cause while considering both short-term and long-term implications for our product.

I'll approach this problem by first clarifying the context, then ruling out external factors before diving deep into the product mechanics, metric breakdown, and data analysis. From there, I'll form hypotheses, conduct root cause analysis, and propose validation methods and solutions.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking there might have been recent changes to the FEP+ algorithm. Have there been any updates to the calculation methodology in the past year?

Why it matters: Changes in methodology could directly impact accuracy. Expected answer: Yes, there have been minor updates. Impact on approach: If yes, we'd focus on these changes; if no, we'd look elsewhere.

• Considering the benchmark comparison, I'm curious about the dataset used. Has the benchmark dataset remained consistent with last year's?

Why it matters: Changes in the dataset could affect perceived accuracy. Expected answer: The dataset is mostly the same, with some additions. Impact on approach: If changed, we'd need to analyze the new dataset's characteristics.

• Thinking about system performance, have there been any changes to the hardware or infrastructure used for these calculations?

Why it matters: Computational resources can significantly affect results. Expected answer: No major changes, but some upgrades were made. Impact on approach: If yes, we'd investigate how these changes might impact calculations.

• Reflecting on user input, has there been any shift in how researchers are setting up or interpreting these calculations?

Why it matters: User behavior can influence perceived accuracy. Expected answer: Some new users have joined the platform recently. Impact on approach: If yes, we'd need to consider training and onboarding processes.