4 citations · 9 across the 9 of their papers we have counts for
9 papers
Accelerating Scientific Discovery with Autonomous Goal-evolving Agents
Yuanqi Du, Botao Yu, Tianyu Liu +25
There has been unprecedented interest in developing agents that expand the boundary of scientific discovery, primarily by optimizing quantitative objective functions specified by s…
Why Pool When You Can Flow? Active Learning with GFlowNets
Renfei Zhang, Mohit Pandey, Artem Cherkasov +1
The scalability of pool-based active learning is limited by the computational cost of evaluating large unlabeled datasets, a challenge that is particularly acute in virtual screeni…
Compositional Flows for 3D Molecule and Synthesis Pathway Co-design
Tony Shen, Seonghwan Seo, Ross Irwin +4
Many generative applications, such as synthesis-based 3D molecular design, involve constructing compositional objects with continuous features. Here, we introduce Compositional Gen…
Pretraining Generative Flow Networks with Inexpensive Rewards for Molecular Graph Generation
Mohit Pandey, Gopeshh Subbaraj, Artem Cherkasov +2
Generative Flow Networks (GFlowNets) have recently emerged as a suitable framework for generating diverse and high-quality molecular structures by learning from rewards treated as…
Causal Order Discovery based on Monotonic SCMs
Ali Izadi, Martin Ester
In this paper, we consider the problem of causal order discovery within the framework of monotonic Structural Causal Models (SCMs), which have gained attention for their potential…
Generative Flows on Synthetic Pathway for Drug Design
Seonghwan Seo, Minsu Kim, Tony Shen +4
Generative models in drug discovery have recently gained attention as efficient alternatives to brute-force virtual screening. However, most existing models do not account for synt…
Geometric-informed GFlowNets for Structure-Based Drug Design
Grayson Lee, Tony Shen, Martin Ester
The rise of cost involved with drug discovery and current speed of which they are discover, underscore the need for more efficient structure-based drug design (SBDD) methods. We em…
TacoGFN: Target-conditioned GFlowNet for Structure-based Drug Design
Tony Shen, Seonghwan Seo, Grayson Lee +5
Searching the vast chemical space for drug-like molecules that bind with a protein pocket is a challenging task in drug discovery. Recently, structure-based generative models have…
Semi-Supervised Junction Tree Variational Autoencoder for Molecular Property Prediction
Atia Hamidizadeh, Tony Shen, Martin Ester
Molecular Representation Learning is essential to solving many drug discovery and computational chemistry problems. It is a challenging problem due to the complex structure of mole…