191 citations · 362 across the 6 of their papers we have counts for
13 papers
Discovering Synergistic Drug Combinations for COVID with Biological Bottleneck Models
Wengong Jin, Regina Barzilay, Tommi Jaakkola
Drug combinations play an important role in therapeutics due to its better efficacy and reduced toxicity. Recent approaches have applied machine learning to identify synergistic co…
Improved Conditional Flow Models for Molecule to Image Synthesis
Karren Yang, Samuel Goldman, Wengong Jin +4
In this paper, we aim to synthesize cell microscopy images under different molecular interventions, motivated by practical applications to drug development. Building on the recent…
Enforcing Predictive Invariance across Structured Biomedical Domains
Wengong Jin, Regina Barzilay, Tommi Jaakkola
Many biochemical applications such as molecular property prediction require models to generalize beyond their training domains (environments). Moreover, natural environments in the…
Adaptive Invariance for Molecule Property Prediction
Wengong Jin, Regina Barzilay, Tommi Jaakkola
Effective property prediction methods can help accelerate the search for COVID-19 antivirals either through accurate in-silico screens or by effectively guiding on-going at-scale e…
Multi-Objective Molecule Generation using Interpretable Substructures
Wengong Jin, Regina Barzilay, Tommi Jaakkola
Drug discovery aims to find novel compounds with specified chemical property profiles. In terms of generative modeling, the goal is to learn to sample molecules in the intersection…
Hierarchical Generation of Molecular Graphs using Structural Motifs
Wengong Jin, Regina Barzilay, Tommi Jaakkola
Graph generation techniques are increasingly being adopted for drug discovery. Previous graph generation approaches have utilized relatively small molecular building blocks such as…