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.
no code implementations • 13 May 2024 • Zhenqiao Song, Yunlong Zhao, Wenxian Shi, Wengong Jin, Yang Yang, Lei LI
In this paper, we propose EnzyGen, an approach to learn a unified model to design enzymes across all functional families.
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.
1 code implementation • 14 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).
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.
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.
no code implementations • 9 Nov 2020 • Wengong Jin, Regina Barzilay, Tommi Jaakkola
Drug combinations play an important role in therapeutics due to its better efficacy and reduced toxicity.
1 code implementation • 15 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.
no code implementations • 6 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.
no code implementations • 5 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.
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.
2 code implementations • ICML 2020 • Wengong Jin, Regina Barzilay, Tommi Jaakkola
Indeed, as we demonstrate, their performance degrades significantly for larger molecules.
4 code implementations • 8 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.
no code implementations • 25 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.
1 code implementation • 11 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.
Ranked #1 on Drug Discovery on QED
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.
4 code implementations • 2 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.
Ranked #3 on Molecular Property Prediction on QM9
no code implementations • 26 Feb 2019 • Guang-He Lee, Wengong Jin, David Alvarez-Melis, Tommi S. Jaakkola
We provide a new approach to training neural models to exhibit transparency in a well-defined, functional manner.
5 code implementations • 3 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.
no code implementations • Chemical Science 2018 • Connor W. Coley, Wengong Jin, Luke Rogers, Timothy F. Jamison, Tommi S. Jaakkola, William H. Green, Regina Barzilay, Klavs F. Jensen
We present a supervised learning approach to predict the products of organic reactions given their reactants, reagents, and solvent(s).
11 code implementations • ICML 2018 • Wengong Jin, Regina Barzilay, Tommi Jaakkola
We evaluate our model on multiple tasks ranging from molecular generation to optimization.
Ranked #1 on Molecular Graph Generation on InterBioScreen
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.
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.