20 citations · 58 across the 10 of their papers we have counts for
8 papers · 1 filter
Active Flow Expansion for Out-of-Distribution Discovery: from Theory to Molecules
Riccardo De Santi, Bruce Lee, Cristian Perez Jensen +6
Standard flow and diffusion pre-training matches the distribution of available data (e.g., molecules), which often covers only a small fraction of the valid design space. In genera…
General Multimodal Protein Design Enables DNA-Encoding of Chemistry
Jarrid Rector-Brooks, Théophile Lambert, Marta Skreta +15
Evolution is an extraordinary engine for enzymatic diversity, yet the chemistry it has explored remains a narrow slice of what DNA can encode. Deep generative models can design new…
Towards equilibrium molecular conformation generation with GFlowNets
Alexandra Volokhova, Michał Koziarski, Alex Hernández-García +7
Sampling diverse, thermodynamically feasible molecular conformations plays a crucial role in predicting properties of a molecule. In this paper we propose to use GFlowNet for sampl…
Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization
Dinghuai Zhang, Ricky T. Q. Chen, Cheng-Hao Liu +2
We tackle the problem of sampling from intractable high-dimensional density functions, a fundamental task that often appears in machine learning and statistics. We extend recent sa…
Thompson sampling for improved exploration in GFlowNets
Jarrid Rector-Brooks, Kanika Madan, Moksh Jain +5
Generative flow networks (GFlowNets) are amortized variational inference algorithms that treat sampling from a distribution over compositional objects as a sequential decision-maki…
Multi-Fidelity Active Learning with GFlowNets
Alex Hernandez-Garcia, Nikita Saxena, Moksh Jain +2
In the last decades, the capacity to generate large amounts of data in science and engineering applications has been growing steadily. Meanwhile, machine learning has progressed to…
GFlowNets for AI-Driven Scientific Discovery
Moksh Jain, Tristan Deleu, Jason Hartford +3
Tackling the most pressing problems for humanity, such as the climate crisis and the threat of global pandemics, requires accelerating the pace of scientific discovery. While scien…
RetroGNN: Approximating Retrosynthesis by Graph Neural Networks for De Novo Drug Design
Cheng-Hao Liu, Maksym Korablyov, Stanisław Jastrzębski +3
De novo molecule generation often results in chemically unfeasible molecules. A natural idea to mitigate this problem is to bias the search process towards more easily synthesizabl…