From Theory to Therapy: Reframing SBDD Model Evaluation via Practical Metrics

TL;DR

This paper proposes a new evaluation framework for structure-based drug design that emphasizes practical metrics over traditional docking scores, aiming to improve real-world applicability.

Contribution

It introduces a comprehensive evaluation framework including similarity measures and virtual screening metrics to better assess practical usability of SBDD models.

Findings

Current models score high on Vina but lack practical usability.

Proposed metrics better predict real-world drug discovery success.

Bridges gap between theoretical predictions and practical application.

Abstract

Recent advancements in structure-based drug design (SBDD) have significantly enhanced the efficiency and precision of drug discovery by generating molecules tailored to bind specific protein pockets. Despite these technological strides, their practical application in real-world drug development remains challenging due to the complexities of synthesizing and testing these molecules. The reliability of the Vina docking score, the current standard for assessing binding abilities, is increasingly questioned due to its susceptibility to overfitting. To address these limitations, we propose a comprehensive evaluation framework that includes assessing the similarity of generated molecules to known active compounds, introducing a virtual screening-based metric for practical deployment capabilities, and re-evaluating binding affinity more rigorously. Our experiments reveal that while current SBDD models achieve high Vina scores, they fall short in practical usability metrics, highlighting a significant gap between theoretical predictions and real-world applicability. Our proposed metrics and dataset aim to bridge this gap, enhancing the practical applicability of future SBDD models and aligning them more closely with the needs of pharmaceutical research and development.