15 citations · 27 across the 7 of their papers we have counts for
13 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…
ChemExplain: A Multimodal Benchmark for Explanatory Chemical Reasoning
Aotian Chen, Frazier N. Baker, Reza Averly +5
Current chemistry benchmarks primarily evaluate outcome prediction or reaction pathway generation, but rarely test whether a model can explain why an observed chemical effect occur…
MADDIE: Multi-Agent Drug Discovery Engine
Ye Liu, Reza Averly, Avery Meyer +4
Drug discovery remains a resource-intensive and time-consuming process, involving billions of dollars and a decade of development to advance a drug to market. While many in silico…
FREA: A Multi-Source Expert Benchmark for Reaction Feasibility Verification
Botao Yu, Bo Zhou, Daniel Adu-Ampratwum +7
As generative models and AI agents propose chemical reactions at a scale beyond expert review, feasibility verifiers decide which proposals enter synthesis planning. But do their d…
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…
MMORF: A Multi-agent Framework for Designing Multi-objective Retrosynthesis Planning Systems
Frazier N. Baker, Trieu Nguyen, Reza Averly +4
Multi-objective retrosynthesis planning is a critical chemistry task requiring dynamic balancing of quality, safety, and cost objectives. Language model-based multi-agent systems (…
LARC: Towards Human-level Constrained Retrosynthesis Planning through an Agentic Framework
Frazier N. Baker, Daniel Adu-Ampratwum, Reza Averly +3
Large language model (LLM) agent evaluators leverage specialized tools to ground the rational decision-making of LLMs, making them well-suited to aid in scientific discoveries, suc…
LIDDIA: Language-based Intelligent Drug Discovery Agent
Reza Averly, Frazier N. Baker, Ian A. Watson +1
Drug discovery is a long, expensive, and complex process, relying heavily on human medicinal chemists, who can spend years searching the vast space of potential therapies. Recent a…
ChemToolAgent: The Impact of Tools on Language Agents for Chemistry Problem Solving
Botao Yu, Frazier N. Baker, Ziru Chen +5
To enhance large language models (LLMs) for chemistry problem solving, several LLM-based agents augmented with tools have been proposed, such as ChemCrow and Coscientist. However,…
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…
LlaSMol: Advancing Large Language Models for Chemistry with a Large-Scale, Comprehensive, High-Quality Instruction Tuning Dataset
Botao Yu, Frazier N. Baker, Ziqi Chen +2
Chemistry plays a crucial role in many domains, such as drug discovery and material science. While large language models (LLMs) such as GPT-4 exhibit remarkable capabilities on nat…
RLSynC: Offline-Online Reinforcement Learning for Synthon Completion
Frazier N. Baker, Ziqi Chen, Daniel Adu-Ampratwum +1
Retrosynthesis is the process of determining the set of reactant molecules that can react to form a desired product. Semi-template-based retrosynthesis methods, which imitate the r…