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Conditional GraphGANFed: Optimizing Graph-Structured Molecule Generation in Federated Generative Adversarial Networks
Daniel Manu, Abee Alazzwi
TL;DR
Molecular GANs need to optimize user-defined properties while preserving realistic chemistry and protecting pharmaceutical data. cGraphGANFed extends GraphGANFed with a critic whose evaluations join discriminator feedback in the generator loss, and simulations report stronger metric performance and robustness than GraphGANFed.
Problem
GraphGANFed preserves privacy through federated learning but cannot generate molecules that specifically optimize user-defined metrics, despite the importance of such objectives for drug discovery.
Method
cGraphGANFed integrates a critic network and graph convolutional network into a federated GAN, combining critic metric evaluations with discriminator feedback in the generator loss.
Results
cGraphGANFed outperforms GraphGANFed particularly in Validity and LogP across settings, with a slight QED advantage when optimizing all metrics and consistently higher QED when targeting QED.
Takeaways & Limitations
The framework generates molecules that preserve chemical similarity to real compounds while optimizing user-defined metrics without compromising data privacy.
Abstract
from arXiv · showhide
Generative adversarial networks (GANs) have garnered considerable attention in molecular discovery for their ability to generate novel and high-quality molecules. To efficiently train a GAN model while preserving data privacy, GraphGANFed has been proposed to incorporate federated learning and graph convolutional networks into GAN. Yet, GraphGANFed cannot produce synthetic molecules that only optimize a user-defined metric(s) to facilitate the new drug discovery process. To address this issue, we introduce a novel extension to GraphGANFed, namely conditional GraphGANFed (cGraphGANFed), by incorporating the critic network to assess generated molecules using user-defined metric(s). The evaluation results from both the critic network and discriminator are integrated into the loss function of the generator, guiding it to generate novel molecules that maintain similar chemical properties to real ones while optimizing user-defined metrics. Extensive simulations are conducted in two scenarios. First, cGraphGANFed endeavors to optimize all seven commonly used metrics, and the results show that cGraphGANFed significantly outperforms GraphGANFed in Validity and LogP, with a slight advantage in QED, across different settings. Second, cGraphGANFed focuses solely on optimizing QED, and the results show that the synthetic molecules produced by cGraphGANFed can achieve more than 10% improvement in QED than GraphGANFed. Also, the results demonstrate cGraphGANFed has enhanced resilience against mode collapses and performance reduction caused by non-IID data.
I. INTRODUCTION
Molecular generation must navigate a vast, discrete chemical space while addressing property optimization and pharmaceutical data-privacy constraints. cGraphGANFed extends GraphGANFed with critic-guided metric optimization, federated training, and graph-based molecule generation.
- Drug discovery seeks novel molecules with optimized chemical properties, but chemical-space exploration is difficult because small structural changes can substantially alter properties.
- Deep generative models still struggle to produce valid, innovative molecules that optimize physical, chemical, and biological objectives because these objectives are complex and non-differentiable.
- Privacy regulations and concerns hinder acquisition of comprehensive molecular datasets needed by reinforcement-learning molecular-generation frameworks.
- cGraphGANFed incorporates a critic network into GraphGANFed to assess generated molecules using user-defined metrics and guide generator training.
- Federated learning avoids sharing molecule samples among pharmaceutical clients during model training.
- Simulations evaluate optimization of individual metrics and all metrics simultaneously, including dependent metrics such as QED, Validity, and LogP.
II. BACKGROUND AND RELATED WORKS
This section introduces GANs and prior reinforcement-learning approaches for molecular generation, then motivates metric optimization and explains the section’s focus. GANs learn to make synthetic samples resemble real data through generator–discriminator competition.
- A. Background of GANs: GANs train a generator and discriminator competitively to reduce the discrepancy between synthetic and real data distributions.
- A. Background of GANs: The generator maps random noise to synthetic samples, while the discriminator estimates whether samples are real and guides adversarial training.
- B. Related Work: Reinforcement learning has been integrated with GANs, including ORGAN’s policy-gradient approach for optimizing molecular generation toward specific objectives.
- C. Overview: The section outlines user-defined metric optimization, cGraphGANFed’s architecture, and metrics for evaluating synthetic molecules.
A. User-defined Metric Optimization in Molecule Discovery
Molecular generation must produce useful properties such as synthesizability, not merely chemically valid structures. The proposed approach addresses this by adding critic-based metric evaluation to adversarial training and measuring multiple molecular qualities.
- De novo drug design extends beyond chemical validity to properties such as synthesizability.
- A. User-defined Metric Optimization in Molecule Discovery: WGAN lacks a mechanism to inform the generator about generated molecules’ user-defined metric performance.
- A. User-defined Metric Optimization in Molecule Discovery: The critic network evaluates generated-molecule metric values and supplies feedback for user-defined optimization.
- Molecular Metrics: Validity measures the proportion of generated molecules that are valid, while uniqueness and novelty measure distinctiveness among valid molecules and difference from the existing dataset.
- Molecular Metrics: Internal Diversity estimates diversity within generated compounds, and QED indicates the probability that a molecule is a potential drug candidate.
- Molecular Metrics: LogP describes concentration relationships between octanol and water phases, while SNN averages fingerprint similarity to each generated molecule’s nearest real-dataset neighbor.
B. The cGraphGANFed Framework
cGraphGANFed extends GraphGANFed with a critic network and federated training to generate graph-structured molecules that preserve real-molecule properties while optimizing user-defined metrics.
- cGraphGANFed integrates a generator, discriminator, critic, and federated-learning system for graph-based molecular generation.
- Generator: The generator produces molecular graphs from noise as node-label and adjacency matrices, with categorical sampling and Gumbel-Softmax enabling gradient-based training.
- Generator: The generator loss combines Wasserstein adversarial feedback with predicted user-defined metric values, controlled by λ to balance chemical similarity and metric optimization.
- Discriminator: The R-GCN-based discriminator distinguishes generated from real molecules using a gradient penalty to stabilize its gradients.
- Critic network: The R-GCN-based critic predicts metric values for molecular graphs, using sigmoid outputs and mean squared error against ground-truth metrics.
- FL system: Clients concurrently train the generator, discriminator, and critic, after which the federated server aggregates and broadcasts the models without sharing local datasets.
IV. SIMULATION SETUPS AND RESULTS
The simulations compare cGraphGANFed with GraphGANFed to evaluate cGraphGANFed’s molecular-generation performance.
- The experiments compare cGraphGANFed and GraphGANFed across the reported simulation settings.
A. Simulation setups
The evaluation uses three benchmark molecular datasets under IID and non-IID federated-data distributions, with fixed training and optimization settings.
- Experiments use ESOL, QM8, and QM9, each split into training, validation, and testing sets with an 80:10:10 ratio.
- Client data are allocated under both IID and non-IID settings, with molecules grouped by molecular-formula classes.
- The generator uses a 16-element standard-normal noise vector, batch size B =16, λ = 0.5, and learning rate 0.0001 in all simulations.
- Local training uses E = 5,000 epochs for ESOL and E = 1,000 epochs for QM8 and QM9, with Adam optimization and gradient penalty coefficient γ = 10.
1) Critic convergence analysis:
On QM8, the generator, discriminator, and critic converge in both IID and non-IID federated settings with similar convergence rates.
- Three clients train the generator, discriminator, and critic for 100 global rounds on QM8 under IID and non-IID settings.
- All three models converge in both data-distribution settings with similar convergence rates.
2) Optimization of all the metrics:
Across IID and non-IID settings, cGraphGANFed is evaluated against GraphGANFed while optimizing seven molecular metrics, with results favoring cGraphGANFed particularly in Validity and LogP.
- Experimental setup: cGraphGANFed and GraphGANFed are compared under IID settings across datasets and client counts using their selected model structures.The comparison covers 3, 4, and 5 clients; the model structures were selected through prior testing and vary by dataset type and client number.
- IID results: cGraphGANFed significantly outperforms GraphGANFed in Validity and LogP, with a slight QED advantage across different datasets and client counts.For ESOL with 5 clients, Validity is 73.2 versus 4.5 and LogP is 0.98 versus 0.88; QED advantages also appear for QM8 and QM9 with five clients.
- Non-IID results: cGraphGANFed and GraphGANFed both lose Validity and LogP under non-IID data, but cGraphGANFed experiences a smaller decrease.The reported comparisons use 3 and 7 clients and support greater robustness of cGraphGANFed to non-IID performance degradation.
- Targeted QED optimization: When QED alone is optimized, cGraphGANFed produces higher QED than GraphGANFed, including 0.55 versus 0.50 and 0.56 versus 0.50 for QM9 with 3 and 4 IID clients.These values correspond to improvements of 10% and 12%, respectively; the paper notes that increasing λ may yield further enhancement.
4) Resolution of Mode Collapse with Critic Network:
cGraphGANFed is presented as more resistant to mode collapse than GraphGANFed when large discriminator models are trained on the small ESOL dataset.
- Mode-collapse setting: GraphGANFed exhibits mode collapse with large discriminator models trained on the small ESOL dataset.Here, mode collapse means that the generator produces repetitive synthetic molecules with very low Uniqueness, typically less than 1.
- Mode-collapse results: cGraphGANFed prevents the reported mode collapse in two large-discriminator instances by attaining 100 Uniqueness, compared with 0.9 for GraphGANFed.The instances cover both IID and non-IID settings.
- Mode-collapse results: The higher Uniqueness achieved by cGraphGANFed is accompanied by much lower Validity than GraphGANFed in these mode-collapse comparisons.The paper relates this trade-off to the inverse correlation between Validity and Uniqueness.
- Overall robustness: The conclusion identifies cGraphGANFed as resilient to mode collapse while also mitigating performance reduction caused by non-IID data.This conclusion summarizes the reported simulation results across the two robustness challenges.