Thao Nguyen

Thao Nguyen, Kuan-Hao Huang, Ge Liu et al.

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 graphs. The key idea behind FARM is the incorporation of functional group (FG) annotations at the atomic level, enabling both FG-enhanced SMILES and FG graphs. In this representation, SMILES strings are enriched with functional group information that identifies the group membership of each atom, while the FG graph captures molecular structure by representing how functional groups are connected. This tokenization injects chemical knowledge into SMILES and expands the effective molecular vocabulary, making the representation more suitable for Transformer-based models and more aligned with natural language structure. FARM learns molecular representations from two complementary perspectives to jointly encode functional and structural information. Masked language modeling on FG-enhanced SMILES captures atom-level features enriched with functional context, while graph neural networks model higher-level molecular topology through functional group connectivity. Contrastive learning is then used to align these two views into a unified embedding space, ensuring that both atom-level detail and functional group structure are jointly represented. We evaluate FARM on the MoleculeNet benchmark and achieve state-of-the-art performance on 8 out of 13 tasks. We further validate its generalization ability on a photostability dataset for quantum mechanical properties. These results demonstrate that FARM improves molecular representation learning, supports strong transfer learning across drug discovery and materials science, and enables broad applications in pharmaceutical research and functional material design.

Sicheng Mo, Thao Nguyen, Richard Zhang et al.

In this work, we explore an untapped signal in diffusion model inference. While all previous methods generate images independently at inference, we instead ask if samples can be generated collaboratively. We propose Group Diffusion, unlocking the attention mechanism to be shared across images, rather than limited to just the patches within an image. This enables images to be jointly denoised at inference time, learning both intra and inter-image correspondence. We observe a clear scaling effect - larger group sizes yield stronger cross-sample attention and better generation quality. Furthermore, we introduce a qualitative measure to capture this behavior and show that its strength closely correlates with FID. Built on standard diffusion transformers, our GroupDiff achieves up to 32.2% FID improvement on ImageNet-256x256. Our work reveals cross-sample inference as an effective, previously unexplored mechanism for generative modeling.

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 generative models without access to external tools or lack the multi-agent coordination and shared memory needed for iterative, evidence-driven reasoning across the molecular design pipeline. MolLingo addresses this by coordinating a Literature Agent, a Chemist Agent, and an Orchestrator through a shared memory module, with each agent equipped with domain-specific tools. To enable effective molecular reasoning, we introduce BRICS-based Fragment Enumeration (BFE), a synthesis-aware molecular fragmentation method that decomposes molecules into chemically meaningful building blocks represented as block-based SMILES paired with common chemical names. This representation bridges molecular structure and LLM semantic space, enabling block-level reasoning and editing that is difficult with raw SMILES alone. As a case study in early-stage therapeutic design, MolLingo further grounds the Chemist Agent's reasoning in binding site geometry and residue-level protein context derived from molecular docking to optimize molecules for stronger target binding. Across four benchmarks, MolLingo consistently outperforms frontier LLMs and specialized baselines, including a fourfold docking score improvement over GPT-5.4 despite using the same underlying model, consistent drug property optimization gains across multiple LLM backbones, and state-of-the-art results on TOMG-Bench, surpassing both frontier LLMs and the RL-based optimization method RePO. Our results suggest that LLMs are already capable molecular design assistants when guided through chemically meaningful representations and biologically grounded structural context. Code is available at: https://anonymous.4open.science/status/MolLingo-7450.