papers

Publications (6)

q-bio.QM2026

Generative structural elucidation from mass spectra as an iterative optimization problem

Mrunali Manjrekar, Runzhong Wang, Samuel Goldman +2

Liquid chromatography tandem mass spectrometry (LC-MS/MS) is a critical analytical technique for molecular identification across metabolomics, environmental chemistry, and chemical…

cs.LG2025

Batched Bayesian optimization by maximizing the probability of including the optimum

Jenna Fromer, Runzhong Wang, Mrunali Manjrekar +3

Batched Bayesian optimization (BO) can accelerate molecular design by efficiently identifying top-performing compounds from a large chemical library. Existing acquisition strategie…

cs.LG2026

MassSpecGym in the Wild: Uncovering and Correcting Evaluation Pitfalls in AI-Driven Molecule Discovery

Hongxuan Liu, Roman Bushuiev, Ivy Lightheart +12

Reliable benchmarking is critical for developing machine learning models for tandem mass spectrometry (MS/MS) based molecule discovery. Subtle issues in experimental design and mod…

cs.LG2025

DiffMS: Diffusion Generation of Molecules Conditioned on Mass Spectra

Montgomery Bohde, Mrunali Manjrekar, Runzhong Wang +2

Mass spectrometry plays a fundamental role in elucidating the structures of unknown molecules and subsequent scientific discoveries. One formulation of the structure elucidation ta…

cs.LG2026

FRIGID: Scaling Diffusion-Based Molecular Generation from Mass Spectra at Training and Inference Time

Montgomery Bohde, Hongxuan Liu, Mrunali Manjrekar +4

Tandem mass spectrometry is prominent in scientific discovery workflows for identifying unknown small molecules, yet high-throughput structural elucidation remains challenging. Whi…

cs.LG2025

Neural Graph Matching Improves Retrieval Augmented Generation in Molecular Machine Learning

Runzhong Wang, Rui-Xi Wang, Mrunali Manjrekar +1

Molecular machine learning has gained popularity with the advancements of geometric deep learning. In parallel, retrieval-augmented generation has become a principled approach comm…