17 papers
LLM-Guided Retrieval for Prediction of Molecular Perturbation Responses
Betty Xiong, Jan-Christian Huetter, Gabriele Scalia +2
Predicting transcriptomic responses to small-molecule perturbations across cell lines is central to drug discovery, but exhaustive profiling of drug-cell combinations is infeasible…
Iterative Distillation for Reward-Guided Fine-Tuning of Diffusion Models in Biomolecular Design
Xingyu Su, Xiner Li, Masatoshi Uehara +7
We address the problem of fine-tuning diffusion models for reward-guided generation in biomolecular design. While diffusion models have proven highly effective in modeling complex,…
Knowledgeable Language Models as Black-Box Optimizers for Personalized Medicine
Michael S. Yao, Osbert Bastani, Alma Andersson +3
The goal of personalized medicine is to discover a treatment regimen that optimizes a patient's clinical outcome based on their personal genetic and environmental factors. However,…
HypoGeneAgent: A Hypothesis Language Agent for Gene-Set Cluster Resolution Selection Using Perturb-seq Datasets
Ying Yuan, Xing-Yue Monica Ge, Aaron Archer Waterman +8
Large-scale single-cell and Perturb-seq investigations routinely involve clustering cells and subsequently annotating each cluster with Gene-Ontology (GO) terms to elucidate the un…
Efficient Fine-Tuning of Single-Cell Foundation Models Enables Zero-Shot Molecular Perturbation Prediction
Sepideh Maleki, Jan-Christian Huetter, Kangway V. Chuang +3
Predicting transcriptional responses to novel drugs provides a unique opportunity to accelerate biomedical research and advance drug discovery efforts. However, the inherent comple…
Fine-Tuning Discrete Diffusion Models via Reward Optimization with Applications to DNA and Protein Design
Chenyu Wang, Masatoshi Uehara, Yichun He +7
Recent studies have demonstrated the strong empirical performance of diffusion models on discrete sequences across domains from natural language to biological sequence generation.…