DGFN: Double Generative Flow Networks
Deep learning is emerging as an effective tool in drug discovery, with potential applications in both predictive and generative models. Generative Flow Networks (GFlowNets/GFNs) are a recently introduced method recognized for the ability to generate diverse candidates, in particular in small molecule generation tasks. In this work, we introduce double GFlowNets (DGFNs). Drawing inspiration from reinforcement learning and Double Deep Q-Learning, we introduce a target network used to sample trajectories, while updating the main network with these sampled trajectories. Empirical results confirm that DGFNs effectively enhance exploration in sparse reward domains and high-dimensional state spaces, both challenging aspects of de-novo design in drug discovery.
Code (0)
등록된 구현이 없습니다.
Tasks
Drug DiscoveryQ-Learningreinforcement-learningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
A Dynamic-Growing Fuzzy-Neuro Controller, Application to a 3PSP Parallel Robot
To date, various paradigms of soft-Computing have been used to solve many modern problems. Among them, a self organizing combination of fuzzy systems and neural networks can make a powerful decision making system. Here, …
Decision MakingMulti-Agent Trajectory Prediction with Difficulty-Guided Feature Enhancement Network
Trajectory prediction is crucial for autonomous driving as it aims to forecast the future movements of traffic participants. Traditional methods usually perform holistic inference on the trajectories of agents, neglectin…
Autonomous DrivingDecoderMotion ForecastingPrediction+1DoubleGen: Debiased Generative Modeling of Counterfactuals
Generative models for counterfactual outcomes face two key sources of bias. Confounding bias arises when approaches fail to account for systematic differences between those who receive the intervention and those who do n…
Deformable Gabor Feature Networks for Biomedical Image Classification
In recent years, deep learning has dominated progress in the field of medical image analysis. We find however, that the ability of current deep learning approaches to represent the complex geometric structures of many me…
ClassificationDeep LearningGeneral Classificationimage-classification+4GDR-learners: Orthogonal Learning of Generative Models for Potential Outcomes
Various deep generative models have been proposed to estimate potential outcomes distributions from observational data. However, none of them have the favorable theoretical property of general Neyman-orthogonality and, a…