A. G. Green

Chemical space separation: Scaffold and UMAP splits are closer to random than we think

FARM: Forecasting Antibiotic Resistance in Mycobacterium tuberculosis using biophysics and machine learning

Antibiotic-resistant tuberculosis remains a major public health challenge, and rapid diagnosis of resistant infections based on genomic markers holds promise for improving time to effective treatment. However, the vast majority of clinically observed variants in resistance-associated genes remain of uncertain significa...

Towards Sparse Causal Features for Zero-shot Mutation Effect Prediction in a Protein Language Model

This work provides the first causal, feature-level account of zero-shot mutation effect prediction in a pLM, and introduces a sparse feature circuit framework that combines sparse autoencoders, integrated-gradients attribution, and activation patching to identify the latent features that causally mediate zero-shot muta...

ActiveFusion: Fused Representations Improve Active Learning for Molecular Property Prediction

This work introduces ActiveFusion, a framework for integrating heterogeneous molecular representations within active learning workflows for molecular property prediction and offers practical guidance for designing active learning pipelines that balance prediction accuracy with chemical space exploration.

Elite-Weighted Supervised Fine-tuning for Goal-Directed Molecular Optimization

This work introduces Elite-Weighted Supervised Fine-tuning (EW-SFT), which uses reward to guide elite selection of high-scoring molecules, and updates the model by its own pretraining loss on that set, and consistently outperforms the corresponding native optimizers.