52 citations · 60 across the 7 of their papers we have counts for
9 papers
MFG: Multi-turn Feedback Guided Molecular Optimization
Trieu Nguyen, Frazier N. Baker, Ruoxi Gao +2
Multi-objective molecular optimization is a critical process in drug discovery that aims to modify a given molecule to simultaneously improve multiple properties while maintaining…
RIDE: Reference-Anchored Inference-Time Diffusion Editing for Scaffold Hopping
Ruoxi Gao, Frazier N. Baker, Trieu Nguyen +1
Scaffold hopping is a critical task in drug discovery, which seeks to discover new, structurally distinct molecules that share key functional groups and similar 3D shape with a ref…
Generating Developable 3D Molecules via Pocket-Conditioned Diffusion and Property-Aware Optimization
Ruoxi Gao, Jiangweizhi Peng, Ziqi Chen +10
Drug discovery and development is time-consuming and resource-intensive, motivating computational approaches such as diffusion models for de novo drug design. Many such models foll…
AutoSDT: Scaling Data-Driven Discovery Tasks Toward Open Co-Scientists
Yifei Li, Hanane Nour Moussa, Ziru Chen +16
Despite long-standing efforts in accelerating scientific discovery with AI, building AI co-scientists remains challenging due to limited high-quality data for training and evaluati…
Large Language Models for Controllable Multi-property Multi-objective Molecule Optimization
Vishal Dey, Xiao Hu, Xia Ning
In real-world drug design, molecule optimization requires selectively improving multiple molecular properties up to pharmaceutically relevant levels, while maintaining others that…
GeLLMO: Generalizing Large Language Models for Multi-property Molecule Optimization
Vishal Dey, Xiao Hu, Xia Ning
Despite recent advancements, most computational methods for molecule optimization are constrained to single- or double-property optimization tasks and suffer from poor scalability…
ScienceAgentBench: Toward Rigorous Assessment of Language Agents for Data-Driven Scientific Discovery
Ziru Chen, Shijie Chen, Yuting Ning +17
The advancements of large language models (LLMs) have piqued growing interest in developing LLM-based language agents to automate scientific discovery end-to-end, which has sparked…
Enhancing Molecular Property Prediction with Auxiliary Learning and Task-Specific Adaptation
Vishal Dey, Xia Ning
Pretrained Graph Neural Networks have been widely adopted for various molecular property prediction tasks. Despite their ability to encode structural and relational features of mol…
as a Two-Step Graph Generative Models for Retrosynthesis Prediction
Ziqi Chen, Oluwatosin R. Ayinde, James R. Fuchs +2
Retrosynthesis is a procedure where a target molecule is transformed into potential reactants and thus the synthesis routes can be identified. Recently, computational approaches ha…