2 citations · 3 across the 7 of their papers we have counts for
8 papers
SpecOpt: Contact-Diff Reasoning for Agentic Molecule Optimization Toward Binding Specificity
Thao Nguyen, Heng Ji
Off-target protein binding is a major source of adverse effects for small-molecule drugs, yet most structure-based molecular design methods focus on generating selective compounds…
EnSol: an environment-aware graph neural network for molecular solubility prediction
Thao Nguyen, Saman Shafaei, Zhengyi Zhang +2
Molecular solubility directly affects key aspects of molecular development such as reaction feasibility, formulation performance, separation efficiency, and solvent selection. Howe…
MolLingo: Molecule-Native Representations for LLM-Powered Scientific Agents
Thao Nguyen, Heng Ji
We present MolLingo, a multi-agent system that emulates the reasoning process of a chemist to automate molecular design. Existing LLM-based approaches either operate as standalone…
ProteinZero: Self-Improving Protein Generation via Online Reinforcement Learning
Ziwen Wang, Jiajun Fan, Ruihan Guo +3
Protein generative models have shown remarkable promise in protein design, yet their success rates remain constrained by reliance on curated sequence-structure datasets and by misa…
Variational Supervised Contrastive Learning
Ziwen Wang, Jiajun Fan, Thao Nguyen +2
Contrastive learning has proven to be highly efficient and adaptable in shaping representation spaces across diverse modalities by pulling similar samples together and pushing diss…
mCLM: A Modular Chemical Language Model that Generates Functional and Makeable Molecules
Carl Edwards, Chi Han, Gawon Lee +11
Despite their ability to understand chemical knowledge, large language models (LLMs) remain limited in their capacity to propose novel molecules with desired functions (e.g., drug-…
FARM: Enhancing Molecular Representations with Functional Group Awareness
Thao Nguyen, Kuan-Hao Huang, Ge Liu +3
We introduce Functional Group-Aware Representations for Small Molecules (FARM), a novel foundation model designed to bridge the gap between SMILES, natural language, and molecular…
GLaD: Synergizing Molecular Graphs and Language Descriptors for Enhanced Power Conversion Efficiency Prediction in Organic Photovoltaic Devices
Thao Nguyen, Tiara Torres-Flores, Changhyun Hwang +3
This paper presents a novel approach for predicting Power Conversion Efficiency (PCE) of Organic Photovoltaic (OPV) devices, called GLaD: synergizing molecular Graphs and Language…