Search Results for author:
Found 22 papers, 12 papers with code
Composing Molecules with Multiple Property Constraints
These rationales are identified from molecules as substructures that are likely responsible for each property of interest.
Unsupervised Protein-Ligand Binding Energy Prediction via Neural Euler's Rotation Equation
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.
Antibody-Antigen Docking and Design via Hierarchical Equivariant Refinement
The binding affinity is governed by the 3D binding interface where antibody residues (paratope) closely interact with antigen residues (epitope).
Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design
In this paper, we propose a generative model to automatically design the CDRs of antibodies with enhanced binding specificity or neutralization capabilities.
Mol2Image: Improved Conditional Flow Models for Molecule to Image Synthesis
In this paper, we aim to synthesize cell microscopy images under different molecular interventions, motivated by practical applications to drug development.
Discovering Synergistic Drug Combinations for COVID with Biological Bottleneck Models
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
In this paper, we aim to synthesize cell microscopy images under different molecular interventions, motivated by practical applications to drug development.
Enforcing Predictive Invariance across Structured Biomedical Domains
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.
Adaptive Invariance for Molecule Property Prediction
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.
Improving Molecular Design by Stochastic Iterative Target Augmentation
The property predictor is then used as a likelihood model for filtering candidate structures from the generative model.
Multi-Objective Molecule Generation using Interpretable Substructures
These rationales are identified from molecules as substructures that are likely responsible for each property of interest.
Hierarchical Generation of Molecular Graphs using Structural Motifs
Indeed, as we demonstrate, their performance degrades significantly for larger molecules.
Iterative Target Augmentation for Effective Conditional Generation
Many challenging prediction problems, from molecular optimization to program synthesis, involve creating complex structured objects as outputs.
Hierarchical Graph-to-Graph Translation for Molecules
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
Learning Multimodal Graph-to-Graph Translation for Molecule Optimization
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.
Analyzing Learned Molecular Representations for Property Prediction
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
Functional Transparency for Structured Data: a Game-Theoretic Approach
We provide a new approach to training neural models to exhibit transparency in a well-defined, functional manner.
Learning Multimodal Graph-to-Graph Translation for Molecular Optimization
We evaluate our model on multiple molecular optimization tasks and show that our model outperforms previous state-of-the-art baselines.
A graph-convolutional neural network model for the prediction of chemical reactivity
We present a supervised learning approach to predict the products of organic reactions given their reactants, reagents, and solvent(s).
Junction Tree Variational Autoencoder for Molecular Graph Generation
We evaluate our model on multiple tasks ranging from molecular generation to optimization.
Ranked #1 on
Molecular Graph Generation
on InterBioScreen
Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network
The prediction of organic reaction outcomes is a fundamental problem in computational chemistry.
Deriving Neural Architectures from Sequence and Graph Kernels
The design of neural architectures for structured objects is typically guided by experimental insights rather than a formal process.
