Increasing the greediness of generative flow networks through action-values
Lau, Elaine · eScholarship@McGill (McGill) · 2025
In recent years, deep learning has emerged as a highly promising tool in drug discovery, offering significant reductions in both time and cost associated with the process. Specifically, a novel generative method known as Generative Flow Networks (GFlowNets) has demonstrated promising results by showcasing its ability to generate a diverse pool of candidates for small molecule generation tasks. Recognizing the potential of GFlowNets, it is essential to investigate and improve their ability to generate high reward and diverse samples, which are crucial for drug development process. This thesis proposes an approach to address this challenge in complex and multi-dimensional scenarios, namely Q-learning GFlowNets (QGFN) - an approach which allows to control the greediness of a GFlowNet by leveraging the well-known reinforcement learning (RL) technique of action values. QGFN increases exploration diversity while preserving the ability to generate high-reward candidates. Empirical results show that QGFN effectively generates high-reward samples in a variety of tasks without sacrificing diversity. This work demonstrates significant improvements that can translate into practical applications in the field of drug discovery, contributing to its advancement