3 citations · 4 across the 10 of their papers we have counts for
6 papers · 1 filter
Training Diffusion Language Models for Black-Box Optimization
Zipeng Sun, Can Chen, Ye Yuan +4
We study offline black-box optimization (BBO), aiming to discover improved designs from an offline dataset of designs and labels, a problem common in robotics and DNA with limited…
TransDiffSBDD: Causality-Aware Multi-Modal Structure-Based Drug Design
Xiuyuan Hu, Guoqing Liu, Can Chen +3
Structure-based drug design (SBDD) is a critical task in drug discovery, requiring the generation of molecular information across two distinct modalities: discrete molecular graphs…
3DMolFormer: A Dual-channel Framework for Structure-based Drug Discovery
Xiuyuan Hu, Guoqing Liu, Can Chen +3
Structure-based drug discovery, encompassing the tasks of protein-ligand docking and pocket-aware 3D drug design, represents a core challenge in drug discovery. However, no existin…
ParetoFlow: Guided Flows in Multi-Objective Optimization
Ye Yuan, Can Chen, Christopher Pal +1
In offline multi-objective optimization (MOO), we leverage an offline dataset of designs and their associated labels to simultaneously minimize multiple objectives. This setting mo…
Importance-aware Co-teaching for Offline Model-based Optimization
Ye Yuan, Can Chen, Zixuan Liu +2
Offline model-based optimization aims to find a design that maximizes a property of interest using only an offline dataset, with applications in robot, protein, and molecule design…
Parallel-mentoring for Offline Model-based Optimization
Can Chen, Christopher Beckham, Zixuan Liu +2
We study offline model-based optimization to maximize a black-box objective function with a static dataset of designs and scores. These designs encompass a variety of domains, incl…