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DiffBP: Generative Diffusion of 3D Molecules for Target Protein Binding

Haitao Lin, Yufei Huang, Odin Zhang, Siqi Ma, Meng Liu, Xuanjing Li, Lirong Wu, Jishui Wang, Tingjun Hou, Stan Z. Li

arXiv:2211.11214v4q-bio.BMcs.LG

TL;DR

Generating molecules for specific protein binding sites is difficult because molecular interactions are global, while prior approaches generate atoms sequentially. DiffBP uses target-aware diffusion to denoise whole molecules at the full-atom level. It reports promising performance on affinity, molecule size, and other drug properties, while noting validity and substructure limitations.

  • Problem

    Protein-aware molecule generation must model complex binding-site context and coupled molecular chemistry and coordinates, while autoregressive generation can conflict with global atom interactions.

    Source: §1 · p. 1, §2.1 · p. 2

  • Method

    DiffBP uses target-aware diffusion to denoise a whole molecule’s atom types and 3D coordinates non-autoregressively, with an equivariant network.

    Source: §1 · p. 2

  • Results

    DiffBP shows promising performance compared with previous methods on appropriate molecule size, target-protein affinity, and other drug properties.

    Source: §1 · p. 2

  • Takeaways & Limitations

    The paper presents full-atom diffusion as a physics-aligned approach to generating drug-like molecules with high target-protein binding affinity.

    Source: §6 · pp. 8–9

    The authors report relatively low validity and substructures that deviate from real drug molecules.

    Source: §6 · p. 9

Abstract

from arXiv · show

Generating molecules that bind to specific proteins is an important but challenging task in drug discovery. Previous works usually generate atoms in an auto-regressive way, where element types and 3D coordinates of atoms are generated one by one. However, in real-world molecular systems, the interactions among atoms in an entire molecule are global, leading to the energy function pair-coupled among atoms. With such energy-based consideration, the modeling of probability should be based on joint distributions, rather than sequentially conditional ones. Thus, the unnatural sequentially auto-regressive modeling of molecule generation is likely to violate the physical rules, thus resulting in poor properties of the generated molecules. In this work, a generative diffusion model for molecular 3D structures based on target proteins as contextual constraints is established, at a full-atom level in a non-autoregressive way. Given a designated 3D protein binding site, our model learns the generative process that denoises both element types and 3D coordinates of an entire molecule, with an equivariant network. Experimentally, the proposed method shows competitive performance compared with prevailing works in terms of high affinity with proteins and appropriate molecule sizes as well as other drug properties such as drug-likeness of the generated molecules.

1. Introduction

Protein-targeted molecule generation remains challenging because binding-site context, molecular chemistry and coordinates, and geometric symmetries must all be handled. DiffBP generates full-atom molecules non-autoregressively and shows promising performance on affinity, molecule size, and other drug properties.

1. Introduction

  • Protein-aware molecule generation must account for complex binding-site geometry and amino-acid context, coupled chemical and coordinate distributions, and translation and rotation symmetries.The task also requires data, which the paper says has only recently become available.Source: §1 · p. 1
  • Autoregressive methods generate atom types and coordinates sequentially, despite global atom interactions, and can stop early, producing molecules with too few atoms.The paper describes this as a mismatch with physical molecular systems and the atom-number distribution of drug-like molecules.Source: §1 · pp. 1–2
  • DiffBP uses diffusion denoising and equivariant graph neural networks to generate entire target-aware molecules at the full-atom level.Its procedures also address molecule center-of-mass prediction and atom-number distribution modeling.Source: §1 · p. 2
  • DiffBP shows promising performance on molecule size, target-protein affinity, and other drug properties.The paper presents it as competitive with previous methods.Source: §1 · p. 2

2. Background

The background frames protein-aware molecule generation as learning a conditional distribution while contrasting full-molecule, pair-coupled interactions with sequential atom generation. It reviews continuous and discrete diffusion, then explains the SE(3) symmetry requirements and the zero-center-of-mass approach.

2.1. Problem Statement

  • Molecule generation seeks p(M|P), but pair-coupled atom interactions motivate joint full-atom modeling rather than autoregressive conditional distributions.The paper represents molecules and proteins by atom types and 3D coordinates, with the protein context held fixed.Source: §2.1 · p. 2

2.2. Diffusion Models

  • Continuous diffusion progressively adds Gaussian noise to data and learns a reverse denoising process, often by predicting the added noise.The described schedules corrupt retained signals toward a standard Gaussian distribution.Source: §2.2 · pp. 2–3
  • Discrete diffusion uses categorical transitions; the absorbing variant masks categories over time and trains a network to predict the clean state from the corrupted one.The masking probability increases through the steps, and the training objective weights contributions by timestep.Source: §2.2 · p. 3

2.3. Equivariance

  • For molecular coordinates, equivariant transitions and an invariant learned distribution support SE(3) generalization; DiffBP fixes molecular center of mass at zero.The coordinates are diffused and denoised in the zero-sum subspace, while the protein remains unchanged.Source: §2.3 · p. 3

3. Method

The method diffuses molecular coordinates and atom types under a fixed protein context, then uses an equivariant denoiser and a pre-generation model to generate molecules.

3.1. Target-aware Diffusion Process

  • The forward process noises molecular coordinates and discrete atom types while leaving the protein unchanged and keeping intermediate molecule centers of mass at zero.Element types use an absorbing diffusion state.Source: §3.1 · pp. 3–4

3.2. Equivariant Graph Denoiser

  • The denoiser is an EGNN that processes molecule and protein atoms and predicts atom-type probabilities and coordinate noise.The paper reports close empirical performance between EGNN and Geometric Vector Perceptron alternatives.Source: §3.2 · p. 4, §3.3 · p. 4

3.3. Optimization Objective

  • Training combines coordinate-noise prediction and masked-type cross-entropy with an intersection regularizer that keeps molecule atoms outside the protein interior.The cross-entropy loss is calculated only for absorbing-type atoms.Source: §3.3 · pp. 4–5

3.4. Generative Denoising Process

  • During reverse generation, atom positions are sampled from a Gaussian transition, while each atom's element type is recovered only once.The fraction of atoms selected for element-type prediction at each step is (T − t)/T.Source: §3.4 · p. 5
  • The pre-generation model predicts molecule size and center of mass from the protein context before denoising, addressing unknown generation inputs.The protein system is translated relative to the predicted molecular center of mass.Source: §3.4 · p. 6

4. Related Work

Related work covers 3D molecular generation and structure-based drug design, including methods that model target-conditioned molecules with grid occupancy or autoregressive atom generation.

3D molecule generation

  • Molecular conformation generation produces stable 3D structures from chemical formulas, while de novo generation creates chemical formulas and 3D structures from scratch.Score-based methods, including diffusion models, are used for conformation generation.Source: §4 · p. 6

Structure-based drug design

  • Structure-based drug design conditions both 2D formulas and 3D structures on target proteins; prior methods use grid occupancy [37] or autoregressively generate atoms.Pocket2Mol and GraphBP model each next atom's type and position and report high validity and target affinity.Source: §4 · p. 6

5. Experiments

Experiments compare DiffBP with autoregressive baselines on binding affinity, molecule size, drug-like properties, substructures, and module choices. DiffBP generates mostly medium-sized molecules and achieves the lowest MPBG and highest ΔBinding, while further analyses examine substructure patterns and regularization effects.

5.1. Setup

  • Evaluation uses CrossDocked2020 pocket structures with the same training/test split, benchmarks 3DSBDD, Pocket2Mol, and GraphBP, and generates 100 molecules for each protein pocket to calculate metrics.Gnina scores affinity; ∆Binding measures the percentage of generated molecules with higher predicted affinity than the reference, with higher values better. MPBG measures mean model performance.Source: §5.1 · p. 6

5.2. Property Evaluation

  • Molecules generated by DiffBP are of medium size, and DiffBP achieves the lowest MPBG and highest ΔBinding among the compared methods.Source: §5.2 · p. 7
  • DiffBP’s other drug-property metrics are described as competitive, while affinity scoring tends to favor larger molecules because they can make more protein interactions.The authors note that reference-molecule size can reflect binding-site size to some degree, but reference molecules are not a gold standard.Source: §5.2 · p. 7, Table 2 · p. 7

5.3. Sub-structure Analysis

  • DiffBP tends to generate more bonds and pentagonal rings, an effect the authors attribute to its larger atom counts; Pocket2Mol generates fewer triangular and more hexagonal rings.Table 3 compares average substructure ratios with reference molecules in the training and test sets.Source: §5.3 · p. 8, Table 3 · p. 8

5.4. Further Analysis on Modules

  • Intersection-loss regularization slightly improves validity, but excessive coefficients reduce it as atoms may collapse into a small region and exceed allowed valences.The workflow's pre-generation model also yields affinity scores comparable to using reference-molecule centers of mass and atom numbers.Source: §5.4 · p. 8, Figure 3 · p. 8

6. Conclusion

DiffBP uses diffusion to generate full-atom molecules with high target-protein affinity. The conclusion also acknowledges relatively low validity and substructure deviations from real drug molecules.

6. Conclusion

  • DiffBP is a diffusion-based 3D molecule generator intended to produce drug-like molecules with high binding affinity to target proteins.The generation process models molecules at the full-atom level.Source: §6 · p. 8
  • Table 4 reports affinity for DiffBP(Pre-Gen) and DiffBP(Pre-Ref).The supplied passage identifies the table's scope but gives no cell values or comparison outcome.Source: Table 4 · p. 9
  • The authors identify relatively low validity and deviations from real drug-molecule substructures as remaining limitations.The conclusion names both constraints.Source: §6 · p. 9

A.1. Analysis on invariance in diffusion models

The appendix discusses how diffusion transformations affect equivariance and describes diffusion schedules and the sampling procedure. It fixes intermediate molecule centers of mass at zero to address translation behavior.

A.1. Analysis on invariance in diffusion models

  • Fixing the center of mass of input positions and each intermediate state to zero addresses the lack of translational equivariance in intermediate diffusion states.The text says this zero-CoM trick makes the translation t equal 0.Source: §A.1 · p. 12
  • Under rotation, intermediate diffusion states satisfy rotation invariance, and the reverse process requires rotation-equivariant conditional predictions.The passage connects this requirement to invariance of generated intermediate-state probabilities.Source: §A.1 · p. 12

A.2. Details in diffusion models

  • Diffusion uses a simple polynomial noise schedule, and the appendix provides a sampling algorithm that outputs a molecule as atom types and positions.The algorithm takes a zero-centered protein, pretrained network, and graph denoiser as inputs; it samples initial positions and iterates denoising.Source: §A.2 · p. 12
  • During sampling, the procedure recovers atom chemical types for selected atoms, with the recovery set defined by I_rcv.The supplied algorithm text describes sampling the recovery set and identifies I_rcv as the index set of atoms needing chemical-type recovery.Source: §A.2 · p. 12

B. Experiments

The experiments include a Pocket2Mol binding-affinity result without UFF refinement and examine how drug-property scores vary with molecule size. QED and LPSK favor smaller molecules, while SA favors larger ones.

B.1. Binding Affinity

  • Table B1 reports Pocket2Mol binding affinity without UFF refinement.The authors state that UFF refinement was not mentioned for Pocket2Mol and provide the without-UFF affinity separately.Source: §B.1 · p. 12, Table B1 · p. 12

B.2. Detailed drug properties

  • QED and LPSK scores favor smaller molecules, whereas SA favors larger molecules.The appendix motivates size-split reporting because binding scores correlate strongly with molecule size.Source: §B.2 · p. 12

B.3. Atom type analysis

The atom-type analysis compares heavy-atom element proportions with the reference distribution. DiffBP and Pocket2Mol most closely match common-element ratios, while DiffBP generates excess sulfur and chlorine.

B.3. Atom type analysis

  • For common elements C, O, and N, DiffBP and Pocket2Mol produce average heavy-atom ratios closest to the true distribution.The analysis counts percentages of heavy atoms.Source: §B.3 · pp. 12–13
  • DiffBP generates more sulfur and chlorine than the reference for uncommon elements.Source: §B.3 · p. 13

B.4. Implementation details

The implementation details cover the evaluation setup, GVP model configuration, training settings, computing platform, and an atom-type-ratio table.

Datasets

  • The training set contains 10,000 protein–ligand paired samples, with 99981 valid molecules; testing uses 100 protein pockets and generates 100 molecules per sample in each trial.Source: §B.4 · p. 13

Hyperparameters

  • The pre-generation model uses 12-layer GVP and the graph denoiser uses 9-layer GVP, selected because GVP was more numerically stable than EGNN in practice.Vertex hidden dimensions are 256 for invariant atom-type attributes and 64 for equivariant geometry positions.Source: §B.4 · p. 13
  • Training uses batch size 8, initial learning rate 10−4, the AdamW optimizer, and the Exponential Moving Average training trick.Source: §B.4 · p. 13

Platforms

  • Each trial runs on a single NVIDIA A100 GPU with 81920 MiB; the code uses Python 3.9, mainly PyTorch 1.12, on Ubuntu Linux.Source: §B.4 · p. 13

Table B3

  • Table B3 reports atom-type ratios for different methods, but the supplied passage gives no ratio values.Source: Table B3 · p. 14

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