828 citations · 1.7k across the 17 of their papers we have counts for
19 papers
Where Should Physics Enter a Molecular Crystal Generator?
Haocheng Tang, Junmei Wang, Wengong Jin
Generative models make molecular crystal structure prediction fast, but their samples still exhibit geometric and packing violations. Physics can be introduced during training, pos…
From Surfaces to Volumes: Registered Geometry for Protein Representation Learning
Siyuan Chen, Cai Zhou, Jinrui Zhang +8
Existing protein geometry models typically represent molecular surfaces using local geometric features such as sampled points, normals, and curvature. While effective for capturing…
Unsupervised Protein-Ligand Binding Energy Prediction via Neural Euler's Rotation Equation
Wengong Jin, Siranush Sarkizova, Xun Chen +2
Protein-ligand binding prediction is a fundamental problem in AI-driven drug discovery. Prior work focused on supervised learning methods using a large set of binding affinity data…
Antibody-Antigen Docking and Design via Hierarchical Equivariant Refinement
Wengong Jin, Regina Barzilay, Tommi Jaakkola
Computational antibody design seeks to automatically create an antibody that binds to an antigen. The binding affinity is governed by the 3D binding interface where antibody residu…
Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design
Wengong Jin, Jeremy Wohlwend, Regina Barzilay +1
Antibodies are versatile proteins that bind to pathogens like viruses and stimulate the adaptive immune system. The specificity of antibody binding is determined by complementarity…
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…
Improving Molecular Design by Stochastic Iterative Target Augmentation
Kevin Yang, Wengong Jin, Kyle Swanson +2
Generative models in molecular design tend to be richly parameterized, data-hungry neural models, as they must create complex structured objects as outputs. Estimating such models…
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…
Hierarchical Graph-to-Graph Translation for Molecules
Wengong Jin, Regina Barzilay, Tommi Jaakkola
The problem of accelerating drug discovery relies heavily on automatic tools to optimize precursor molecules to afford them with better biochemical properties. Our work in this pap…
Analyzing Learned Molecular Representations for Property Prediction
Kevin Yang, Kyle Swanson, Wengong Jin +12
Advancements in neural machinery have led to a wide range of algorithmic solutions for molecular property prediction. Two classes of models in particular have yielded promising res…
Functional Transparency for Structured Data: a Game-Theoretic Approach
Guang-He Lee, Wengong Jin, David Alvarez-Melis +1
We provide a new approach to training neural models to exhibit transparency in a well-defined, functional manner. Our approach naturally operates over structured data and tailors t…
Learning Multimodal Graph-to-Graph Translation for Molecular Optimization
Wengong Jin, Kevin Yang, Regina Barzilay +1
We view molecular optimization as a graph-to-graph translation problem. The goal is to learn to map from one molecular graph to another with better properties based on an available…
Junction Tree Variational Autoencoder for Molecular Graph Generation
Wengong Jin, Regina Barzilay, Tommi Jaakkola
We seek to automate the design of molecules based on specific chemical properties. In computational terms, this task involves continuous embedding and generation of molecular graph…
Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network
Wengong Jin, Connor W. Coley, Regina Barzilay +1
The prediction of organic reaction outcomes is a fundamental problem in computational chemistry. Since a reaction may involve hundreds of atoms, fully exploring the space of possib…
Deriving Neural Architectures from Sequence and Graph Kernels
Tao Lei, Wengong Jin, Regina Barzilay +1
The design of neural architectures for structured objects is typically guided by experimental insights rather than a formal process. In this work, we appeal to kernels over combina…