9 papers
When Three-Dimensional Conformer Ensembles Improve Molecular Property Prediction Beyond Two-Dimensional Fingerprints: A Systematic Study
Bryan Cheng, Austin Jin, Jasper Zhang
When do three-dimensional conformer ensembles improve molecular property prediction beyond two-dimensional fingerprints? We provide the first systematic, mechanistically grounded a…
Structure-Aware Prediction of PROTAC-Mediated Protein Degradability via Graph Neural Networks
Bryan Cheng, Austin Jin
Proteolysis-targeting chimeras (PROTACs) can selectively degrade disease-causing proteins, yet predicting which targets are amenable to degradation remains a critical bottleneck: e…
SpliceBind: Isoform-Aware Prediction of Binding Pocket Druggability
Bryan Cheng, Austin Jin, Joshua Chang
Splice-mediated drug resistance occurs in up to 40% of patients on targeted kinase inhibitors, yet state-of-the-art druggability tools operate on single structures and cannot compa…
Single-Position Intervention Fails: Distributed Output Templates Drive In-Context Learning
Bryan Cheng, Jasper Zhang
Understanding how large language models encode task identity from few-shot demonstrations is a central open problem in mechanistic interpretability. Prior work uses linear probing…
State Space Models are Effective Sign Language Learners: Exploiting Phonological Compositionality for Vocabulary-Scale Recognition
Bryan Cheng, Austin Jin, Jasper Zhang
Sign language recognition suffers from catastrophic scaling failure: models achieving high accuracy on small vocabularies collapse at realistic sizes. Existing architectures treat…
ECLIPSE: A Composable Pipeline for Predicting ecDNA Formation, Evolution, and Therapeutic Vulnerabilities in Cancer
Bryan Cheng, Jasper Zhang
Extrachromosomal DNA (ecDNA) represents one of the most pressing challenges in cancer biology: circular DNA structures that amplify oncogenes, evade targeted therapies, and drive t…
When Does Context Help? A Systematic Study of Target-Conditional Molecular Property Prediction
Bryan Cheng, Jasper Zhang
We present the first systematic study of when target context helps molecular property prediction, evaluating context conditioning across 10 diverse protein families, 4 fusion archi…
The Mechanistic Invariance Test: Genomic Language Models Fail to Learn Positional Regulatory Logic
Bryan Cheng, Jasper Zhang
Genomic language models (gLMs) have transformed computational biology, achieving state-of-the-art performance across genomic tasks. Yet a fundamental question threatens the foundat…
Information-Theoretic Requirements for Gradient-Based Task Affinity Estimation in Multi-Task Learning
Jasper Zhang, Bryan Cheng
Multi-task learning shows strikingly inconsistent results -- sometimes joint training helps substantially, sometimes it actively harms performance -- yet the field lacks a principl…