Connor W. Coley

Interaction Profiles as a Universal Language for Generative Molecular Design with ShEPhERD-2

This paper introduces ShEPhERD-2, a 3D generative model that creates drug-like molecules based on interaction profiles, demonstrating its versatility across multiple design tasks.

Decoding protein language models: insights from embedding space analysis

This paper introduces a framework for comparing embedding spaces in molecular biology, demonstrating its utility in pre-filtering machine learning tasks and identifying differential patterns across embeddings.

Neural Spectral Prediction for Structure Elucidation with Tandem Mass Spectrometry

The paper introduces a deep learning model, ICEBERG, for structural elucidation using tandem mass spectrometry, achieving high accuracy in identifying molecular structures from complex mixtures.

Identification of Antituberculars with Favorable Potency and Pharmacokinetics through Structure-Based and Ligand-Based Modeling

The paper develops a hybrid modeling pipeline to identify antitubercular compounds with favorable potency and pharmacokinetics, resulting in 12 promising candidates from a large compound library.

EnzymeCAGE: A Geometric Foundation Model for Enzyme Retrieval with Evolutionary Insights

EnzymeCAGE is a geometric foundation model trained on enzyme-reaction pairs to predict enzyme function, de-orphan reactions, identify catalytic sites, and reconstruct biosynthetic pathways.

Annotating metabolite mass spectra with domain-inspired chemical formula transformers

This paper introduces MIST, a deep learning framework for metabolite structure annotation from mass spectra, outperforming existing methods and identifying new metabolites in a disease cohort.