mlsanityData to 7 Oct 2026

Goal-conditioned GFlowNets for Controllable Multi-Objective Molecular Design

arXiv 2306.04620 ↗First posted 7 Jun 2023cs.LG q-bio.BMcited by 17

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Abstract (arXiv)

In recent years, in-silico molecular design has received much attention from the machine learning community. When designing a new compound for pharmaceutical applications, there are usually multiple properties of such molecules that need to be optimised: binding energy to the target, synthesizability, toxicity, EC50, and so on. While previous approaches have employed a scalarization scheme to turn the multi-objective problem into a preference-conditioned single objective, it has been established that this kind of reduction may produce solutions that tend to slide towards the extreme points of the objective space when presented with a problem that exhibits a concave Pareto front. In this work we experiment with an alternative formulation of goal-conditioned molecular generation to obtain a more controllable conditional model that can uniformly explore solutions along the entire Pareto front.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 24 of its 42 references that are in the map. First posted on arXiv 7 Jun 2023.

Builds on

24 of 42 references.

References · 24

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  2. 2016Concrete Problems in AI Safetycited by 1,582
  3. 2017Hindsight Experience Replaycited by 1,096
  4. 2017MoleculeNet: A Benchmark for Molecular Machine Learningcited by 1,037
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  6. 2019Graph Transformer Networkscited by 520
  7. 2021Flow Network based Generative Models for Non-Iterative Diverse Candidate Generationcited by 384
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12 more
  1. 2018Optimization of Molecules via Deep Reinforcement Learningcited by 199
  2. 2022Biological Sequence Design with GFlowNetscited by 172
  3. 2022Bayesian Structure Learning with Generative Flow Networkscited by 153
  4. 2022Learning GFlowNets from partial episodes for improved convergence and stabilitycited by 129
  5. 2022GFlowNets and variational inferencecited by 103
  6. 2022Multi-Objective GFlowNetscited by 100
  7. 2023A theory of continuous generative flow networkscited by 98
  8. 2023Better Training of GFlowNets with Local Credit and Incomplete Trajectoriescited by 83
  9. 2023Robust Scheduling with GFlowNetscited by 67
  10. 2023GFlowNet-EM for learning compositional latent variable modelscited by 40
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  12. 2022Unifying Generative Models with GFlowNets and Beyondcited by 24

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 9; 2025: 6; 2026: 1.

17 papers.

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  1. 2024RGFN: Synthesizable Molecular Generation Using GFlowNetscited by 39
  2. 2024Flow of Reasoning: Training LLMs for Divergent Reasoning with Minimal Examplescited by 29
  3. 2024SynFlowNet: Design of Diverse and Novel Molecules with Synthesis Constraintscited by 26
  4. 2023Order-Preserving GFlowNetscited by 13
  5. 2024RetroGFN: Diverse and Feasible Retrosynthesis using GFlowNetscited by 10
  6. 2024Looking Backward: Retrospective Backward Synthesis for Goal-Conditioned GFlowNetscited by 8
  7. 2025Virtual Cells: Predict, Explain, Discovercited by 7
  8. 2024Small Molecule Optimization with Large Language Modelscited by 5
  9. 2024Cell Morphology-Guided Small Molecule Generation with GFlowNetscited by 4
  10. 2024GFlowNet Pretraining with Inexpensive Rewardscited by 4
  11. 2025Pretraining Generative Flow Networks with Inexpensive Rewards for Molecular Graph Generationcited by 3
  12. 2024Using GNN property predictors as molecule generatorscited by 1
5 more
  1. 2025Curriculum-Augmented GFlowNets For mRNA Sequence Generationcited by 1
  2. 2025Global-Order GFlowNetscited by 0
  3. 2025Beyond the Proxy: Trajectory-Distilled Guidance for Offline GFlowNet Trainingcited by 0
  4. 2025GFlowNets for Learning Better Drug-Drug Interaction Representationscited by 0
  5. 2026Particle GFlowNets: Rethinking Generative Marginalization Modelscited by 0