Context-Guided Diffusion for Out-of-Distribution Molecular and Protein Design (2407.11942v1)

Abstract: Generative models have the potential to accelerate key steps in the discovery of novel molecular therapeutics and materials. Diffusion models have recently emerged as a powerful approach, excelling at unconditional sample generation and, with data-driven guidance, conditional generation within their training domain. Reliably sampling from high-value regions beyond the training data, however, remains an open challenge -- with current methods predominantly focusing on modifying the diffusion process itself. In this paper, we develop context-guided diffusion (CGD), a simple plug-and-play method that leverages unlabeled data and smoothness constraints to improve the out-of-distribution generalization of guided diffusion models. We demonstrate that this approach leads to substantial performance gains across various settings, including continuous, discrete, and graph-structured diffusion processes with applications across drug discovery, materials science, and protein design.

• The paper demonstrates that Context-Guided Diffusion (CGD) significantly improves the generation of novel molecules and proteins beyond the training distribution.
• CGD integrates unlabeled context data with a regularization term to ensure smooth, calibrated guidance without altering model architecture.
• Empirical results reveal CGD's superiority in producing high-value, diverse compounds for drug discovery, material design, and protein engineering.

Context-Guided Diffusion for Out-of-Distribution Molecular and Protein Design

The paper "Context-Guided Diffusion for Out-of-Distribution Molecular and Protein Design" proposes a novel method for enhancing the performance of property-guided diffusion models, particularly in generating high-value samples beyond the training distribution. This method, termed Context-Guided Diffusion (CGD), uses unlabeled data and introduces a regularization term that biases guidance model training towards functions that generalize well in out-of-distribution scenarios.

Introduction

Molecular discovery involves identifying novel compounds with desirable properties within vast search spaces, often hindered by the bias and scarcity of labeled data. The paper addresses the challenge of reliable sample generation in regions of high value beyond the training data. With context-guided diffusion, the focus is on improving out-of-distribution generalization, leveraging unlabeled data and smoothness constraints.

Guidance models traditionally suffer from poor generalization under distribution shifts, presenting challenges in property-guided diffusion models. The paper proposes using a guidance model regularizer that enhances generalization capabilities, particularly under distribution shifts, thereby enabling Context-Guided Diffusion (CGD).

Figure 1: Guidance models that generalize poorly under distribution shifts can be a major performance bottleneck for property-guided diffusion models. We introduce a guidance model regularizer that improves generalization under distribution shifts and enables context-guided diffusion.

Guided Diffusion Models

Diffusion models condition sample generation processes to produce outcomes with specific properties. Traditional approaches, such as classifier-free guidance, require explicit input of conditional information, which limits applicability chiefly to classification tasks. In contrast, the CGD method is adept for regression-based optimization problems, frequently encountered in molecular design.

Context-Guided Diffusion Models

The CGD approach involves constructing a regularization term that leverages context data to promote uncertainty in guidance model predictions outside the training distribution. This method does not necessitate altering the guidance model’s architecture nor does it introduce computational overhead during sampling. Implementation involves integrating context batches during guidance model training, ensuring well-calibrated guidance signals.

Figure 2: {\em Context-guided diffusion} leverages unlabeled context data to combine signals from labeled training data with structural information of the broader input domain (left).

Empirical Evaluation

The paper demonstrates the efficacy of CGD across multiple tasks, including graph-structured diffusion for small molecules, equivariant diffusion for materials, and discrete diffusion for protein sequences. In each scenario, CGD outperformed baseline models by generalizing better to high-value, out-of-distribution subsets of the chemical and protein sequence space.

Graph-Structured Diffusion for Small Molecules

Figure 3: Comparison of the small molecules generated with different guided diffusion models across five distinct protein targets.

CGD shows improved capability in generating small molecules with high docking scores, a crucial performance aspect in drug discovery.

Equivariant Diffusion For Materials

Figure 4: Comparison of polycyclic aromatic systems generated with different guidance models across ten independent training and sampling runs.

For material design, CGD effectively resulted in the generation of novel compounds with desirable electronic properties, outperforming traditional methods and demonstrating superior sample diversity.

Discrete Diffusion for Protein Sequences

Figure 5: Pareto fronts of samples generated with different regularization schemes, highlighting the trade-off between objective value and naturalness.

CGD models consistently produced more effective results for protein design, especially in scenarios requiring high out-of-distribution generalization.

Discussion and Limitations

While CGD performs robustly across several domains, its computational cost during training may be higher compared to conventional methods. Moreover, selecting hyperparameters and constructing maximally informative context sets necessitates domain expertise.

Figure 6: A visualization of the Swiss roll dataset used to train different guidance models.

Conclusion

Context-Guided Diffusion presents a substantial advancement in the generation of novel molecular and protein structures, aligning exploration and optimization effectively in out-of-distribution scenarios. Future research could focus on integrating CGD with active learning strategic to amplify context set construction and derivative prediction methodologies.