DiffDock-L

What is DiffDock-L?

DiffDock-L is the latest version of the molecular docking tool developed at MIT that uses diffusion models to predict how small molecule ligands bind to protein targets. It treats molecular docking as a generative modeling task rather than traditional optimization, using the same technology that powers image generators like DALL-E and Stable Diffusion.

Building upon the original DiffDock published at ICLR 2023, DiffDock-L (released at ICLR 2024) achieves significantly better accuracy and generalization to novel protein targets.

DiffDock-L vs. DiffDock

DiffDock-L introduced several enhancements over the original DiffDock:

Enhanced accuracy: DiffDock-L achieves 43% success rate for ligand RMSD <2Å compared to the original DiffDock's 38%. On the DockGen benchmark designed to test generalization across protein domains, success rate improved from 10% to 24%.

Better generalization: The model uses ECOD (Evolutionary Classification of protein Domains) structure-based cluster sampling during training to ensure better performance on proteins with novel binding pocket architectures.

Larger model & more training data: DiffDock-L scales from 4M to 30M parameters and benefits from increased training data and novel synthetic data generation (extracting side chains from protein structures as ligands).

Confidence bootstrapping: A novel self-training scheme where the diffusion model and confidence model work together to fine-tune on unseen protein domains without structural data.

Optimized output: By default, DiffDock-L outputs 10 high-quality predictions compared to the previous 40, reducing computation time by approximately 2x while maintaining superior accuracy.

How does DiffDock-L work?

Unlike traditional docking methods that use physics-based scoring functions and optimization algorithms, DiffDock-L applies diffusion models to molecular docking. The key innovation is treating docking as a generative modeling task over a non-Euclidean manifold rather than an optimization problem.

Diffusion process on SE(3) manifold

DiffDock-L defines a diffusion process over the degrees of freedom involved in docking: the ligand's position relative to the protein (translation), its orientation in the binding pocket (rotation), and the torsion angles describing its conformation. This maps to the SE(3) manifold—the Special Euclidean group in 3D space that represents all possible rigid body transformations.

The model starts with a random ligand pose and iteratively denoises it through learned reverse diffusion steps to converge on plausible binding poses. Unlike traditional methods that optimize a single pose, this generative approach naturally produces diverse binding modes.

Graph representation

Protein and ligand structures are represented as heterogeneous geometric graphs. Ligand atoms and protein residues serve as nodes, with residues receiving initial features from language model embeddings trained on protein sequences.

Nodes are sparsely connected based on distance cutoffs that depend on node types and diffusion timestep, allowing efficient message passing while capturing relevant interactions.

Embedding message-passing layers

DiffDock-L adds embedding message-passing layers that independently process protein and ligand structures before cross-attention. Unlike DiffDock's purely cross-attentional layers, this allows increased architectural depth with minimal runtime overhead.

Under the rigid protein assumption, protein embeddings are computed once and cached across all diffusion steps and samples, providing efficiency gains.

SE(3) equivariant neural networks

The model uses geometric deep learning with SE(3) equivariance—rotations and translations of the input don't change the predicted binding physics, only the coordinate frame. This physical symmetry is encoded through specialized graph neural networks that process geometric information while respecting 3D symmetries, improving generalization and data efficiency.

Confidence model

For each generated pose, DiffDock-L predicts a confidence score estimating the likelihood that the pose has RMSD <2Å to the true binding mode. This confidence model is trained jointly with the diffusion model and enables automatic ranking of generated poses without requiring external scoring functions.

Input requirements

DiffDock-L requires two inputs:

• Protein structure: A PDB file or PDB ID from RCSB PDB database. The protein should be prepared (protonated, cleaned) for best results—use PDB Fixer to prepare it.
• Ligand: SMILES string (most common for small molecules), SDF, or MOL file format.

Docking parameters

Number of poses: How many binding poses to generate. We recommend starting with 10-20 poses for general use. Increase to 30-40 if exploring diverse binding modes—more poses increase computation time proportionally.

Inference steps: Number of diffusion denoising steps. The model was trained with 20 steps. Research suggests 15 is optimal for speed/accuracy balance.

Actual steps: Number of steps actually executed (usually inference_steps - 2). This is an advanced parameter for optimization.

No final step noise: Removes noise from the final denoising step for more stable and deterministic predictions. We recommend keeping this enabled.

Understanding the results

DiffDock-L generates multiple binding poses ranked by confidence.

Rank

Poses are sorted by confidence score, with Rank 1 being the most confident prediction.

Confidence score

A model-predicted score indicating confidence in the binding pose. Higher (less negative) scores indicate higher confidence:

• Score > -1.5: High confidence
• Score -1.5 to -2.5: Moderate confidence
• Score < -2.5: Lower confidence

The confidence model estimates the probability that a pose has RMSD <2Å to the true binding mode.

Multiple poses

We recommend examining multiple top-ranked poses:

• Different poses may represent alternative binding modes
• The highest-ranked pose isn't always correct
• Ensemble analysis provides more robust predictions

Best practices

Protein preparation

Clean structures yield better results:

• Remove water molecules unless they're critical for binding
• Add missing hydrogens and residues
• Ensure proper protonation states at physiological pH
• Use PDB Fixer for automated preparation

Result validation

• Examine top 3-5 ranked poses, not just the top one
• Check for reasonable protein-ligand interactions (H-bonds, hydrophobic contacts)
• Validate critical predictions with molecular dynamics or experimental methods

Limitations

DiffDock-L has known limitations that may affect certain use cases:

• Does not account for protein flexibility (rigid receptor assumption)
• Performance may vary for large or macrocyclic ligands
• Metalloprotein binding requires special consideration
• Trained primarily on x-ray structures; performance on computationally folded structures is reduced but still superior to traditional methods

Comparison to traditional docking

Advantages of DiffDock-L

• Superior accuracy: 43% success rate (RMSD <2Å) versus 23% for traditional methods on PDBBind
• Better handling of flexible ligands: Generative approach naturally models conformational flexibility
• No manual parameter tuning: Works out-of-the-box without exhaustive search parameters
• Diverse poses: Diffusion sampling generates multiple binding modes automatically
• Confidence estimates: Built-in confidence model for pose ranking

Traditional methods (AutoDock Vina, GNINA)

• Faster computation: Optimization-based approaches can be quicker for simple systems
• More interpretable: Physics-based scoring functions provide mechanistic insights
• Established validation: Decades of validation in drug discovery pipelines
• Specialized cases: Better support for metals, covalent binding, and specific constraints

For most drug discovery applications, DiffDock-L provides the best balance of accuracy and ease of use.

Related tools

ProteinIQ offers several molecular docking tools, each with different strengths:

• AutoDock Vina — Physics-based docking with interpretable energy scores. Faster for simple systems and better for metalloproteins.
• GNINA — Combines traditional physics-based docking with deep learning CNN scoring. Good balance of speed and accuracy.
• Smina — Fork of AutoDock Vina with additional scoring functions and minimization options.
• ColabDock — Protein-protein docking using distance constraints.
• PocketFlow — Structure-based generative design for de novo ligand generation.

For analyzing your ligand properties before docking, see ADMET-AI for pharmacokinetic predictions or Lipinski's Rule of Five for drug-likeness assessment.