5 papers
Uncertainty-Calibrated Diffusion for Reliable 3D Molecular Graph Generation
Fang Wan, Jingxiang Qu, Yi Liu
Bayesian inference provides a principled framework for modeling epistemic uncertainty in neural networks by treating predictions as distributions rather than deterministic values.…
Purely Agent-Driven Black-Box Optimization for Biological Design
Natalie Maus, Yimeng Zeng, Haydn Thomas Jones +11
Many key challenges in biological design -- such as small-molecule drug discovery, antimicrobial peptide development, and protein engineering -- can be framed as black-box optimiza…
Covering Multiple Objectives with a Small Set of Solutions Using Bayesian Optimization
Natalie Maus, Kyurae Kim, Yimeng Zeng +5
In multi-objective black-box optimization, the goal is typically to find solutions that optimize a set of black-box objective functions, , simultaneously. Trad…
Predicting and generating antibiotics against future pathogens with ApexOracle
Tianang Leng, Fangping Wan, Marcelo Der Torossian Torres +1
Antimicrobial resistance (AMR) is escalating and outpacing current antibiotic development. Thus, discovering antibiotics effective against emerging pathogens is becoming increasing…
Large Scale Multi-Task Bayesian Optimization with Large Language Models
Yimeng Zeng, Natalie Maus, Haydn Thomas Jones +7
In multi-task Bayesian optimization, the goal is to leverage experience from optimizing existing tasks to improve the efficiency of optimizing new ones. While approaches using mult…