10 citations · 21 across the 10 of their papers we have counts for
10 papers
GLACIER: Rethinking Mass Spectrum Prediction as an Object Detection Problem
Rui-Xi Wang, Runzhong Wang, Connor W. Coley
Predicting tandem mass spectra (MS/MS) from molecular structures represents a central task in analytical chemistry with direct relevance to clinical metabolomics, systems biology,…
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…
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…
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…
Optimization of Robotic Liquid Handling as a Capacitated Vehicle Routing Problem
Guangqi Wu, Runzhong Wang, Connor W. Coley
We present an optimization strategy to reduce the execution time of liquid handling operations in the context of an automated chemical laboratory. By formulating the task as a capa…
On the Expressive Power of Subgraph Graph Neural Networks for Graphs with Bounded Cycles
Ziang Chen, Qiao Zhang, Runzhong Wang
Graph neural networks (GNNs) have been widely used in graph-related contexts. It is known that the separation power of GNNs is equivalent to that of the Weisfeiler-Lehman (WL) test…
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…
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…
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…
Double-Ended Synthesis Planning with Goal-Constrained Bidirectional Search
Kevin Yu, Jihye Roh, Ziang Li +3
Computer-aided synthesis planning (CASP) algorithms have demonstrated expert-level abilities in planning retrosynthetic routes to molecules of low to moderate complexity. However,…