Geometric Deep Learning for Structure-Based Drug Design: A Survey (2306.11768v6)

Abstract: Structure-based drug design (SBDD) leverages the three-dimensional geometry of proteins to identify potential drug candidates. Traditional approaches, rooted in physicochemical modeling and domain expertise, are often resource-intensive. Recent advancements in geometric deep learning, which effectively integrate and process 3D geometric data, alongside breakthroughs in accurate protein structure predictions from tools like AlphaFold, have significantly propelled the field forward. This paper systematically reviews the state-of-the-art in geometric deep learning for SBDD. We begin by outlining foundational tasks in SBDD, discussing prevalent 3D protein representations, and highlighting representative predictive and generative models. Next, we provide an in-depth review of key tasks, including binding site prediction, binding pose generation, de novo molecule generation, linker design, protein pocket generation, and binding affinity prediction. For each task, we present formal problem definitions, key methods, datasets, evaluation metrics, and performance benchmarks. Lastly, we explore current challenges and future opportunities in SBDD. Challenges include oversimplified problem formulations, limited out-of-distribution generalization, biosecurity concerns related to the misuse of structural data, insufficient evaluation metrics and large-scale benchmarks, and the need for experimental validation and enhanced model interpretability. Opportunities lie in leveraging multimodal datasets, integrating domain knowledge, developing comprehensive benchmarks, establishing criteria aligned with clinical outcomes, and designing foundation models to expand the scope of design tasks. We also curate \url{https://github.com/zaixizhang/Awesome-SBDD}, reflecting ongoing contributions and new datasets in SBDD.

Overview and Insights on "Geometric Deep Learning for Structure-Based Drug Design: A Survey"

The research paper titled "Geometric Deep Learning for Structure-Based Drug Design: A Survey" by Zaixi Zhang et al. presents a comprehensive review of the current state of geometric deep learning (GDL) applications in structure-based drug design (SBDD). This paper provides a systematic review and critical insights into methodologies integrating 3D geometric data for drug discovery processes that leverage the structural complexities of target proteins.

SBDD leverages the three-dimensional information of proteins to guide the identification and optimization of potential therapeutics. The paper highlights how traditional SBDD methods, rooted in physicochemical modeling, are often resource-intensive, while recent advancements such as AlphaFold have drastically improved the accuracy of protein structure predictions. The authors explore how these developments have been incorporated into contemporary GDL frameworks, enhancing SBDD across several key tasks.

Key SBDD Tasks and Methodologies

The survey categorizes applications of geometric deep learning into key tasks:

1. Binding Site Prediction: This involves identifying regions on the protein surface where ligands can potentially bind. Techniques predominantly use 3D representations such as grids, surfaces, and graphs for efficient prediction using CNNs and GNNs.
2. Binding Pose Prediction: Predicting how a ligand binds to a target protein is crucial for understanding protein-ligand interactions. The paper discusses traditional rigid-body docking methods and the emergence of diffusion models like DiffDock, which improve predictions by simulating flexible binding sites.
3. De Novo Ligand Generation: Considered one of the more challenging tasks, it involves generating new ligand structures that bind effectively to target sites. The paper discusses diverse approaches such as autoregressive models and flow models, emphasizing those incorporating chemical priors for more realistic outputs.
4. Linker Design: Involves connecting molecular fragments into complete molecules, crucial in developing proteolysis targeting chimeras (PROTACs). The paper highlights methods using VAE and reinforcement learning frameworks for designing effective linkers within protein pockets.
5. Binding Affinity Prediction: This evaluates the strength of the interaction between a ligand and its protein target. Recent developments incorporate GDL methods that improve prediction accuracy over classical empirical scoring functions.

Challenges and Opportunities

The paper identifies several challenges facing the application of GDL in SBDD:

• Oversimplified Problem Formulations: Many models assume static protein structures, neglecting the inherent flexibility of proteins in real-world scenarios.
• Generalization to New Proteins: There is a need to develop robust models that extrapolate effectively to proteins outside the training dataset, addressing overfitting and component generalization challenges.
• Reliable Evaluation Metrics: Common metrics may not fully capture the utility of the models, pointing to the necessity for metrics accommodating recent advances and offering better benchmarks for comparison.

Furthermore, the paper outlines future opportunities, including:

• Integration with Multimodal Data: Incorporating protein sequences, textual data, and additional omics data can further enhance model robustness and applicability.
• Exploiting Biological and Chemical Knowledge: Leveraging insights from chemistry and biomedicine within modeling frameworks offers another layer of depth that can improve task outcomes.
• Development of Foundation Models: Similar to the current direction in general AI with pre-trained models, there is potential in building generalizable models for SBDD that can perform a broad scope of tasks across diverse data formats and requirements.

Conclusion and Implications

The survey underscores the paradigm shift towards employing geometric deep learning techniques in SBDD, acknowledging the transformative potential these methods bring to the field. These innovations enable a deeper understanding of drug-target interactions and offer pathways to more effective therapeutic discoveries. The research concludes that while geometric deep learning presents a promising avenue, continued exploration of these identified challenges and opportunities will be critical for its advancement in drug discovery contexts.