6 citations · 19 across the 10 of their papers we have counts for
10 papers
Evaluating Large Language Models in Scientific Discovery
Zhangde Song, Jieyu Lu, Yuanqi Du +53
Large language models (LLMs) are increasingly applied to scientific research, yet prevailing science benchmarks probe decontextualized knowledge and overlook the iterative reasonin…
Accelerating inverse materials design using generative diffusion models with reinforcement learning
Junwu Chen, Jeff Guo, Edvin Fako +1
Diffusion models promise to accelerate material design by directly generating novel structures with desired properties, but existing approaches typically require expensive and subs…
KL-Regularized Reinforcement Learning is Designed to Mode Collapse
Anthony GX-Chen, Jatin Prakash, Jeff Guo +2
It is commonly believed that optimizing the reverse KL divergence results in "mode seeking", while optimizing forward KL results in "mass covering", with the latter being preferred…
Generative Molecular Design with Steerable and Granular Synthesizability Control
Jeff Guo, Víctor Sabanza-Gil, Olha Semenenko +20
Designing molecules that are both property-optimal and readily synthesizable is a central challenge in drug discovery. Existing works that do consider synthesizability can jointly…
LLM-Augmented Chemical Synthesis and Design Decision Programs
Haorui Wang, Jeff Guo, Lingkai Kong +4
Retrosynthesis, the process of breaking down a target molecule into simpler precursors through a series of valid reactions, stands at the core of organic chemistry and drug develop…
Tango*: Constrained synthesis planning using chemically informed value functions
Daniel Armstrong, Zlatko Joncev, Jeff Guo +1
Computer-aided synthesis planning (CASP) has made significant strides in generating retrosynthetic pathways for simple molecules in a non-constrained fashion. Recent work introduce…
Directly Optimizing for Synthesizability in Generative Molecular Design using Retrosynthesis Models
Jeff Guo, Philippe Schwaller
Synthesizability in generative molecular design remains a pressing challenge. Existing methods to assess synthesizability span heuristics-based methods, retrosynthesis models, and…
Saturn: Sample-efficient Generative Molecular Design using Memory Manipulation
Jeff Guo, Philippe Schwaller
Generative molecular design for drug discovery has very recently achieved a wave of experimental validation, with language-based backbones being the most common architectures emplo…
Beam Enumeration: Probabilistic Explainability For Sample Efficient Self-conditioned Molecular Design
Jeff Guo, Philippe Schwaller
Generative molecular design has moved from proof-of-concept to real-world applicability, as marked by the surge in very recent papers reporting experimental validation. Key challen…
Augmented Memory: Capitalizing on Experience Replay to Accelerate De Novo Molecular Design
Jeff Guo, Philippe Schwaller
Sample efficiency is a fundamental challenge in de novo molecular design. Ideally, molecular generative models should learn to satisfy a desired objective under minimal oracle eval…