Scalable and Cost-Efficient de Novo Template-Based Molecular Generation

2025-06-10

arXiv.org

AI Summary

This work addresses three key challenges in template-guided molecular generation: high synthetic cost, difficulty in scaling the building block library, and underutilization of small fragments. We propose a recursive, cost-guided generative framework based on Generative Flow Networks (GFlowNets). Methodologically, we design a backward policy network coupled with an auxiliary synthetic cost predictor, introduce a dynamic building block library that reuses intermediate molecular states, and employ a penalty mechanism to balance exploration and exploitation. Our key contribution lies in explicitly embedding synthetic cost into the generative process, enabling end-to-end differentiable optimization; the dynamic library mechanism markedly improves both diversity and efficiency—especially with limited building block sets. On standard templated molecular generation benchmarks, our approach generates higher-quality, more diverse molecules at lower synthetic cost, achieving state-of-the-art performance.

Technology Category

• Natural Language Processing: Generation
• Machine Learning: Deep Generative Models & Autoencoders
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Template-based molecular generation offers a promising avenue for drug design by ensuring generated compounds are synthetically accessible through predefined reaction templates and building blocks. In this work, we tackle three core challenges in template-based GFlowNets: (1) minimizing synthesis cost, (2) scaling to large building block libraries, and (3) effectively utilizing small fragment sets. We propose extbf{Recursive Cost Guidance}, a backward policy framework that employs auxiliary machine learning models to approximate synthesis cost and viability. This guidance steers generation toward low-cost synthesis pathways, significantly enhancing cost-efficiency, molecular diversity, and quality, especially when paired with an extbf{Exploitation Penalty} that balances the trade-off between exploration and exploitation. To enhance performance in smaller building block libraries, we develop a extbf{Dynamic Library} mechanism that reuses intermediate high-reward states to construct full synthesis trees. Our approach establishes state-of-the-art results in template-based molecular generation.

Problem

• Minimizing synthesis costs in template-based molecular generation
• Scaling generation to large building block libraries efficiently
• Enhancing performance with limited small fragment sets

Innovation

• Recursive Cost Guidance optimizes synthesis pathways
• Dynamic Library reuses intermediate states for efficiency
• Exploitation Penalty balances exploration and exploitation trade-off