Assessing interaction recovery of predicted protein-ligand poses (2409.20227v1)

Abstract: The field of protein-ligand pose prediction has seen significant advances in recent years, with machine learning-based methods now being commonly used in lieu of classical docking methods or even to predict all-atom protein-ligand complex structures. Most contemporary studies focus on the accuracy and physical plausibility of ligand placement to determine pose quality, often neglecting a direct assessment of the interactions observed with the protein. In this work, we demonstrate that ignoring protein-ligand interaction fingerprints can lead to overestimation of model performance, most notably in recent protein-ligand cofolding models which often fail to recapitulate key interactions.

• The paper critically evaluates protein-ligand pose prediction methods, arguing that assessing the recovery of key molecular interactions using protein-ligand interaction fingerprints (PLIFs) is crucial, beyond traditional RMSD or physical plausibility.
• The study compares traditional docking methods (like GOLD) with machine learning-based approaches (like DiffDock-L and cofolding tools) on the PoseBusters dataset, measuring RMSD, PoseBuster validity, and importantly, PLIF recovery rates.
• Results show that traditional docking tools outperform current ML methods in recovering specific interactions vital for binding, suggesting the need for ML models to incorporate interaction-aware metrics to improve biological relevance in predicted poses.

Assessing Interaction Recovery of Predicted Protein-Ligand Poses

The paper "Assessing interaction recovery of predicted protein-ligand poses" presents a critical evaluation of current methodologies in protein-ligand pose prediction, particularly focusing on the extent to which these tools recapitulate key molecular interactions. The authors emphasize the significance of protein-ligand interaction fingerprints (PLIFs) in the accurate assessment of predicted poses, arguing that traditional metrics such as RMSD (Root-Mean-Squared Deviation) and physical plausibility checks are insufficient for a comprehensive evaluation.

Key Insights and Methodologies

The primary concern of the paper is the tendency of ML techniques, especially recent protein-ligand cofolding models, to overestimate performance by neglecting critical interaction recovery. The authors advocate for a significant shift towards using PLIFs as a benchmark for evaluating ML-based docking and pose prediction tools. They propose that PLIF recovery should be a standard metric, alongside RMSD and PoseBuster's validity, to assess the biological relevance of predicted poses.

To substantiate their analysis, the authors employ a variety of computational techniques comparing traditional docking algorithms, such as FRED, HYBRID, and GOLD, with ML-based methods like DiffDock-L and protein-ligand cofolding tools such as Umol and RoseTTAFold All-Atom. These models are evaluated on the PoseBusters dataset, which provides a set of 256 high-quality protein-ligand complexes. The paper meticulously examines the performance of each method, focusing on RMSD, PoseBuster validity, and the proposed PLIF recovery rates.

Numerical Findings

The results clearly indicate that traditional docking methods, inherently designed to optimize interaction recovery, outperform ML docking algorithms in generating physically plausible poses. For instance, GOLD achieves higher PLIF recovery rates than ML models, even when ML-generated poses align closely with the crystal structure in terms of RMSD. In contrast, cofolding methods underperform significantly, often failing to replicate the critical interactions present in the crystal pose.

An intriguing aspect of the paper is the interaction type analysis, revealing that classical docking tools better recover specific interactions such as hydrogen bonds and $\pi$-stacking interactions, which are abundant and vital for stabilizing ligand binding. The ML methods, however, demonstrate lower recovery rates, reinforcing the notion that the absence of explicit interaction terms in their training process leads to suboptimal pose predictions.

Theoretical and Practical Implications

Theoretically, this paper underscores the need to incorporate interaction-aware metrics in training and evaluating ML-based docking methods to align them more closely with classical methods that prioritize molecular interactions. Practically, for fields like drug discovery, ensuring that predicted poses not only fit geometrically but also replicate known interaction profiles can enhance the biological relevance and efficacy of candidate compounds.

Future developments could focus on refining ML models to internalize interaction-focused scoring functions or loss components that mimic the interaction-seeking nature of classical docking algorithms. Such enhancements could bridge the gap between current ML methodologies and the interaction fidelity observed in classical approaches.

In conclusion, the paper reminds the computational chemistry and bioinformatics communities to exercise careful scrutiny when adopting ML-based predictions, advocating for a reevaluation of metrics that better capture the essence of protein-ligand binding fidelity. This approach could pave the way for more robust application of ML in computational docking, ultimately contributing to more reliable predictions in pharmaceutical research and development.