Deep Learning in Drug Discovery: Predicting Protein-Ligand Binding Affinity

Accurately predicting protein–ligand binding affinity is a cornerstone of drug discovery, enabling the identification and optimization of therapeutic compounds. Advances in deep learning (DL) are revolutionizing this process, offering solutions to the limitations of traditional computational methods. This comprehensive review explores the use of DL in predicting binding affinity, highlighting databases, input representations, challenges, and future directions for improvement.

The Importance of Predicting Binding Affinity

Protein–ligand binding affinity measures the strength of interaction between a protein and a ligand, such as a small organic molecule. This interaction drives many biological processes and determines a drug’s efficacy and specificity. Accurate predictions reduce costs and accelerate drug development by identifying promising candidates early.

Traditional computational methods, such as molecular docking and molecular dynamics, use scoring functions to estimate binding affinity. While effective in theory, these methods are limited by simplified physical models, high computational costs, and reliance on extensive manual feature engineering.

Deep Learning in Protein–Ligand Affinity Prediction

DL models excel at learning complex patterns from high-dimensional data, automatically extracting features that traditional methods struggle to capture. By leveraging neural networks, DL approaches offer significant improvements in accuracy and scalability.

Interaction-Based vs. Interaction-Free Models

1. Interaction-Based Models:
   • Use 3D structures of protein-ligand complexes to extract interaction details.
   • Commonly employ convolutional neural networks (CNNs) or graph neural networks (GNNs) to process voxel grids, contact maps, or interaction graphs.
2. Interaction-Free Models:
   • Rely on sequence data, such as ligand SMILES strings and protein sequences, to predict affinity.
   • While flexible, these models lack direct interaction information and may struggle with accuracy.

The Role of Databases

High-quality databases are crucial for training DL models, providing the foundation for accurate predictions. Notable databases include:

• PDBbind: Contains experimentally measured binding affinities and 3D structural data. It is widely used for structural models and includes subsets for training and benchmarking.
• Davis: Focuses on kinase proteins and inhibitors, offering binding data represented as KdK_dKd​ values.
• KIBA: Optimizes consistency across KdK_dKd​, KiK_iKi​, and IC50IC_{50}IC50​ values, providing a benchmark for kinase-ligand interactions.
• ChEMBL: An open-access repository of bioactivity data for numerous proteins and compounds, suitable for sequence-based models.
• CASF and CSAR: Benchmark datasets for testing DL models’ prediction accuracy.
• Astex Diverse Set: Offers a range of protein-ligand complexes for validating docking algorithms.

Challenges with Databases:

• Imbalanced Data: Many databases have disproportionate negative samples, affecting model performance.
• Limited Diversity: Existing databases focus heavily on kinase proteins, lacking representation for other protein families.
• Dynamic Information: Static datasets fail to capture the dynamic nature of protein-ligand interactions.

Input Representations

The way data is represented significantly impacts DL model performance. Representations include:

1. Ligand Input:
   • SMILES strings for 2D molecular structure.
   • Bioactive properties extracted from datasets like Chemical Checker.
2. Protein Input:
   • Sequence-Based: Encoded as integers, one-hot vectors, or secondary structure features.
   • Structure-Based: Focus on local pockets rather than global structures, represented as 3D grids or point clouds.
3. Protein–Ligand Complex:
   • Local Structure: Encodes physicochemical properties of protein pockets interacting with ligands.
   • Global Structure: Captures long-range interactions using intermolecular contacts or molecular graphs.
4. Environmental Factors:
   • Water networks and other factors influencing protein–ligand interactions are increasingly considered.

Deep Learning Models

DL models in this field leverage various architectures to improve predictions:

• CNNs: Used for both 1D sequence data and 3D voxel grids. While effective, they can struggle with long-range interactions and rotational sensitivity.
• GNNs: Represent protein-ligand interactions as graphs, offering robustness and flexibility. They excel in capturing local interaction details but may miss dynamic or long-range interactions.

State-of-the-Art Models:

• CAPLA: Combines sequence and pocket features for accurate sequence-based predictions.
• HAC-Net and ResAtom: Hybrid models incorporating CNNs and GNNs with attention mechanisms, excelling in structure-based predictions.

Challenges and Future Directions

Challenges:

1. Database Limitations:
   • Imbalanced and kinase-focused datasets limit generalizability.
   • Absence of dynamic structural and environmental data restricts model accuracy.
2. Input Representation:
   • Current methods inadequately capture mutations and dynamic ligand binding processes.
   • Lacking integration of cellular environment data.
3. Model Limitations:
   • CNNs and GNNs face challenges with long-range interactions and dynamic structural changes.
   • Limited standardization in evaluating model performance.

Future Directions:

1. Database Development:
   • Balanced, diverse datasets representing various protein families.
   • Integration of dynamic structural data and environmental conditions.
2. Enhanced Representations:
   • Incorporate predicted mutant protein structures.
   • Utilize dynamic graphs for capturing real-time ligand binding.
3. Model Innovations:
   • Combine sequence- and structure-based approaches.
   • Develop dynamic GNNs for time-resolved predictions.
   • Incorporate attention mechanisms to enhance feature extraction.

Conclusion

Deep learning has ushered in a new era for predicting protein–ligand binding affinity, transforming drug discovery and design. While significant challenges remain, ongoing advancements in databases, input representations, and model architectures promise a future of unprecedented accuracy and efficiency in computational biology.

FAQ: Protein-Ligand Binding Affinity Prediction with Deep Learning

1. What is protein-ligand binding affinity and why is predicting it important?

Protein-ligand binding affinity refers to the strength of interaction between a protein and a small molecule (ligand). This interaction is crucial because many biological processes, including drug action, rely on proteins binding to specific molecules. Accurately predicting binding affinity is vital in drug discovery because it helps identify potential drug candidates, screen for lead compounds, and optimize drug efficacy and specificity. Experimental methods for determining binding affinity are complex, time-consuming, and expensive, making computational prediction methods highly desirable.

2. What are some traditional computational methods for predicting protein-ligand binding affinity, and what are their limitations?

Traditional methods for predicting binding affinity include molecular docking and molecular dynamics simulations. These methods use scoring functions to estimate the strength of protein-ligand interactions based on structural data. Molecular docking, which uses semi-flexible protein-ligand complexes, has lower computational cost but also lower accuracy. Molecular dynamics, which uses flexible complexes, is more accurate, but requires more computational resources. Although scoring functions provide a theoretical basis for binding affinity prediction, they rely on incomplete physical models and simplified approximations, making accurate predictions for large datasets challenging.

3. How do deep learning (DL) models improve upon traditional methods for predicting protein-ligand binding affinity?

DL models, a subset of machine learning, use deep neural networks to automatically extract advanced features from raw data, without extensive manual data processing and feature engineering required by traditional machine learning (ML) approaches. DL models can learn complex patterns from large datasets, enabling more accurate predictions of protein-ligand binding affinity. Furthermore, they can handle high-dimensional data and have strong pattern recognition abilities, overcoming many of the limitations of traditional scoring functions and ML models. The success of AlphaFold2 in protein structure prediction demonstrates the potential of DL in structural biology.

4. What databases are commonly used for training and testing DL models for protein-ligand binding affinity?

Several databases are crucial for DL model development in this field. The PDBbind database provides experimentally measured binding affinities for protein-ligand complexes with known 3D structures, and is often used as a training set for structural models. The Davis and KIBA databases offer large datasets of kinase protein-ligand binding affinities, typically used for training sequence-based models with corresponding SMILES notation of ligands and protein sequences. The ChEMBL database provides extensive bioactivity data. The CASF, CSAR, and Astex diverse datasets serve as benchmarks for testing model performance. These databases vary in content (sequence vs. structure), the number of proteins, ligands, binding affinity values and given information (e.g., binding constants, structural files).

5. How is the input data, including protein and ligand structures and sequences, represented for DL models?

Input representations are critical for model accuracy. For ligands, two-dimensional (2D) structures based on SMILES strings, 2D matrix, or molecular graphs are common, often encoded as integer sequences. Bioactive properties of the ligand can also be used. For proteins, input can be sequence-based using amino acid sequences encoded as integers or one-hot vectors or structure-based using pocket information represented as 3D grids, implicit graphs, or point clouds. Protein-ligand complex representations include local structures (e.g., voxel grids, Cartesian coordinates with atomic features) and global structures (e.g., intermolecular contacts, molecular graphs with distance information). Environment elements, such as water networks, can also be incorporated.

6. What are the main types of DL models used for protein-ligand binding affinity prediction, and how do they differ?

DL models for binding affinity can be broadly categorized as interaction-based and interaction-free. Interaction-based models, which are more commonly used, use protein-ligand complex structures or interaction features as inputs, allowing them to learn the direct interactions between the protein and ligand. Examples include models using 3D convolutional neural networks (CNNs) with voxel grid inputs or models using contact maps or interaction graphs. Interaction-free models predict affinity based on protein sequences, ligand SMILES strings, and potentially pocket/ligand structures, without requiring the protein-ligand complex structure. These models utilize CNNs for feature extraction from sequence or molecular representations or Graph Neural Networks (GNNs) to extract features from the 3D structure of the binding pocket and the 2D graph of the ligand molecule.

7. What are the main challenges in developing accurate DL models for protein-ligand binding affinity prediction?

Several challenges persist. Databases can suffer from small sample sizes, imbalanced classes (e.g., disproportionately negative samples), limited diversity (e.g., most sequence databases focused on kinases), and inconsistent experimental conditions. Input representation methods need to account for residue mutations, cell environmental effects, protein substrate effects and the dynamic nature of protein-ligand interactions, which can change binding poses and atom positions. Model architecture faces the challenge of balancing computational complexity, accurately modeling long-range interactions, and addressing sensitivity to rotations. There is also a need for a more standardized evaluation framework to compare model performance reliably.

8. What are some future directions for improving DL models in this field?

Future work includes developing databases that incorporate balanced datasets, dynamic structural information, and additional data, including mutated protein structures, cell environment data and substrates. Incorporating molecular dynamics simulations to capture dynamic interactions, transforming dynamic structural information into dynamic graphs and using them as inputs for dynamic GNNs are promising avenues. Furthermore, exploring new DL architectures that effectively integrate sequence and structure data, and address the limitations of current CNNs and GNNs can further enhance prediction accuracy and efficiency. Attention mechanisms show promise in improving model performance. More research is required to develop sequence-based models applicable to more diverse protein families as well as creating a standardized evaluation framework to reliably compare the efficacy of different models.

Glossary of Key Terms

• Agonist: A drug that binds to a receptor and activates it to produce a biological effect. Complete agonists have strong binding affinity and intrinsic activity, while partial agonists have strong binding affinity but weak intrinsic activity.
• Antagonist: A drug that binds to a receptor but does not activate it, instead blocking the action of agonists.
• Binding Affinity: A measure of the strength of the interaction between a protein and a ligand, typically quantified by parameters like Ki, Kd, Km, IC50, or EC50, where lower values indicate higher binding affinity.
• Binding Pocket/Site: A specific cavity or groove on the surface of a protein where a ligand binds and interacts with the protein.
• ChEMBL Database: A large, open-access database containing bioactivity data for drug-like molecules and their targets.
• Convolutional Neural Network (CNN): A type of deep learning model that uses convolutional layers to extract local features from multi-dimensional data.
• CSAR Database: The Community Structure-Activity Resource database. Contains crystal structures and binding affinities for protein-ligand complexes, often used for model evaluation.
• Davis Database: A database that provides binding affinity data for kinase proteins and clinically relevant inhibitors.
• Deep Learning (DL): A subset of machine learning that uses neural networks with multiple layers to automatically extract features and learn complex patterns from data.
• Graph Neural Network (GNN): A type of neural network that operates on graph data structures, used to model relationships and interactions between nodes and edges.
• Interaction-Based Model: A deep learning model that uses the 3D structure of a protein-ligand complex as input, allowing it to learn key interactions between the protein and the ligand.
• Interaction-Free Model: A deep learning model that does not require the 3D structure of a protein-ligand complex and instead uses protein sequences, ligand SMILES, and sometimes protein pocket information as inputs.
• KIBA Database: A database derived from a score function that optimizes the consistency between different experimental measures of binding affinity.
• Ligand: A small molecule that binds to a protein to perform a biological function or as a drug.
• Machine Learning (ML): An artificial intelligence technique that enables computer systems to learn patterns from data and make predictions.
• Molecular Docking: A computational method that predicts the preferred orientation of a ligand when bound to a protein.
• Molecular Dynamics Simulation: A computational method that simulates the movement of atoms and molecules to study the dynamic behavior of protein-ligand complexes.
• PDBbind Database: A database that collects experimentally determined binding affinities for protein-ligand complexes and their 3D structures.
• Protein Data Bank (PDB): A database containing 3D structural data for proteins and other macromolecules.
• Root Mean Squared Error (RMSE): A statistical metric used to evaluate the predictive accuracy of a machine learning model. It is the square root of the average of the squared differences between predicted and actual values.
• SMILES: A line notation system for representing the structure of chemical molecules, which stands for Simplified Molecular-Input Line-Entry System.
• Structure-Based Input: A type of input representation that uses the 3D coordinates of atoms in a protein and/or ligand structure.
• Sequence-Based Input: A type of input representation that uses the amino acid or nucleotide sequence of a protein or ligand.
• Voxel: A volume element that represents values on a regular grid in three-dimensional space.

Reference

Wang, H. (2024). Prediction of protein–ligand binding affinity via deep learning models. Briefings in Bioinformatics, 25(2), bbae081.