Search Results for author: Wengong Jin

Found 22 papers, 12 papers with code

Composing Molecules with Multiple Property Constraints

no code implementations ICML 2020 Wengong Jin, Regina Barzilay, Tommi Jaakkola

These rationales are identified from molecules as substructures that are likely responsible for each property of interest.

Drug Discovery

Unsupervised Protein-Ligand Binding Energy Prediction via Neural Euler's Rotation Equation

1 code implementation NeurIPS 2023 Wengong Jin, Siranush Sarkizova, Xun Chen, Nir Hacohen, Caroline Uhler

Specifically, we train an energy-based model on a set of unlabelled protein-ligand complexes using SE(3) denoising score matching and interpret its log-likelihood as binding affinity.

Denoising Drug Discovery

Antibody-Antigen Docking and Design via Hierarchical Equivariant Refinement

1 code implementation14 Jul 2022 Wengong Jin, Regina Barzilay, Tommi Jaakkola

The binding affinity is governed by the 3D binding interface where antibody residues (paratope) closely interact with antigen residues (epitope).

Atomic Forces

Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design

1 code implementation ICLR 2022 Wengong Jin, Jeremy Wohlwend, Regina Barzilay, Tommi Jaakkola

In this paper, we propose a generative model to automatically design the CDRs of antibodies with enhanced binding specificity or neutralization capabilities.

Protein Design Specificity

Mol2Image: Improved Conditional Flow Models for Molecule to Image Synthesis

no code implementations CVPR 2021 Karren Yang, Samuel Goldman, Wengong Jin, Alex X. Lu, Regina Barzilay, Tommi Jaakkola, Caroline Uhler

In this paper, we aim to synthesize cell microscopy images under different molecular interventions, motivated by practical applications to drug development.

Contrastive Learning Image Generation

Discovering Synergistic Drug Combinations for COVID with Biological Bottleneck Models

no code implementations9 Nov 2020 Wengong Jin, Regina Barzilay, Tommi Jaakkola

Drug combinations play an important role in therapeutics due to its better efficacy and reduced toxicity.

Improved Conditional Flow Models for Molecule to Image Synthesis

1 code implementation15 Jun 2020 Karren Yang, Samuel Goldman, Wengong Jin, Alex Lu, Regina Barzilay, Tommi Jaakkola, Caroline Uhler

In this paper, we aim to synthesize cell microscopy images under different molecular interventions, motivated by practical applications to drug development.

Contrastive Learning Image Generation

Enforcing Predictive Invariance across Structured Biomedical Domains

no code implementations6 Jun 2020 Wengong Jin, Regina Barzilay, Tommi Jaakkola

We evaluate our method on multiple applications: molecular property prediction, protein homology and stability prediction and show that RGM significantly outperforms previous state-of-the-art baselines.

Domain Generalization Molecular Property Prediction +1

Adaptive Invariance for Molecule Property Prediction

no code implementations5 May 2020 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 experimental efforts.

Property Prediction Transfer Learning

Improving Molecular Design by Stochastic Iterative Target Augmentation

2 code implementations ICML 2020 Kevin Yang, Wengong Jin, Kyle Swanson, Regina Barzilay, Tommi Jaakkola

The property predictor is then used as a likelihood model for filtering candidate structures from the generative model.

Program Synthesis

Multi-Objective Molecule Generation using Interpretable Substructures

2 code implementations8 Feb 2020 Wengong Jin, Regina Barzilay, Tommi Jaakkola

These rationales are identified from molecules as substructures that are likely responsible for each property of interest.

Drug Discovery

Iterative Target Augmentation for Effective Conditional Generation

no code implementations25 Sep 2019 Kevin Yang, Wengong Jin, Kyle Swanson, Regina Barzilay, Tommi Jaakkola

Many challenging prediction problems, from molecular optimization to program synthesis, involve creating complex structured objects as outputs.

Program Synthesis

Hierarchical Graph-to-Graph Translation for Molecules

1 code implementation11 Jun 2019 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.

Drug Discovery Graph-To-Graph Translation +1

Learning Multimodal Graph-to-Graph Translation for Molecule Optimization

no code implementations ICLR 2019 Wengong Jin, Kevin Yang, Regina Barzilay, Tommi Jaakkola

We evaluate our model on multiple molecule optimization tasks and show that our model outperforms previous state-of-the-art baselines by a significant margin.

Graph-To-Graph Translation Translation

Analyzing Learned Molecular Representations for Property Prediction

4 code implementations2 Apr 2019 Kevin Yang, Kyle Swanson, Wengong Jin, Connor Coley, Philipp Eiden, Hua Gao, Angel Guzman-Perez, Timothy Hopper, Brian Kelley, Miriam Mathea, Andrew Palmer, Volker Settels, Tommi Jaakkola, Klavs Jensen, Regina Barzilay

In addition, we introduce a graph convolutional model that consistently matches or outperforms models using fixed molecular descriptors as well as previous graph neural architectures on both public and proprietary datasets.

Molecular Property Prediction molecular representation +1

Learning Multimodal Graph-to-Graph Translation for Molecular Optimization

5 code implementations3 Dec 2018 Wengong Jin, Kevin Yang, Regina Barzilay, Tommi Jaakkola

We evaluate our model on multiple molecular optimization tasks and show that our model outperforms previous state-of-the-art baselines.

Graph-To-Graph Translation Translation

Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network

1 code implementation NeurIPS 2017 Wengong Jin, Connor W. Coley, Regina Barzilay, Tommi Jaakkola

The prediction of organic reaction outcomes is a fundamental problem in computational chemistry.

Deriving Neural Architectures from Sequence and Graph Kernels

no code implementations ICML 2017 Tao Lei, Wengong Jin, Regina Barzilay, Tommi Jaakkola

The design of neural architectures for structured objects is typically guided by experimental insights rather than a formal process.

Graph Regression Language Modelling +1

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