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Found 15 papers, 7 papers with code
Fusing Neural and Physical: Augment Protein Conformation Sampling with Tractable Simulations
The protein dynamics are common and important for their biological functions and properties, the study of which usually involves time-consuming molecular dynamics (MD) simulations in silico.
Protein Sequence and Structure Co-Design with Equivariant Translation
Proteins are macromolecules that perform essential functions in all living organisms.
E3Bind: An End-to-End Equivariant Network for Protein-Ligand Docking
In silico prediction of the ligand binding pose to a given protein target is a crucial but challenging task in drug discovery.
A Roadmap for Big Model
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm.
GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation
GeoDiff treats each atom as a particle and learns to directly reverse the diffusion process (i. e., transforming from a noise distribution to stable conformations) as a Markov chain.
TorchDrug: A Powerful and Flexible Machine Learning Platform for Drug Discovery
However, lacking domain knowledge (e. g., which tasks to work on), standard benchmarks and data preprocessing pipelines are the main obstacles for machine learning researchers to work in this domain.
Predicting Molecular Conformation via Dynamic Graph Score Matching
However, these non-bonded atoms may be proximal to each other in 3D space, and modeling their interactions is of crucial importance to accurately determine molecular conformations, especially for large molecules and multi-molecular complexes.
Non-Autoregressive Electron Redistribution Modeling for Reaction Prediction
Reliably predicting the products of chemical reactions presents a fundamental challenge in synthetic chemistry.
An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming
Specifically, the molecular graph is first encoded in a latent space, and then the 3D structures are generated by solving a principled bilevel optimization program.
Learning Gradient Fields for Molecular Conformation Generation
We study a fundamental problem in computational chemistry known as molecular conformation generation, trying to predict stable 3D structures from 2D molecular graphs.
MARS: Markov Molecular Sampling for Multi-objective Drug Discovery
Searching for novel molecules with desired chemical properties is crucial in drug discovery.
Non-autoregressive electron flow generation for reaction prediction
Our model achieves both an order of magnitude lower inference latency, with state-of-the-art top-1 accuracy and comparable performance on Top-K sampling.
A Graph to Graphs Framework for Retrosynthesis Prediction
A fundamental problem in computational chemistry is to find a set of reactants to synthesize a target molecule, a. k. a.
Ranked #15 on
Single-step retrosynthesis
on USPTO-50k
GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation
Molecular graph generation is a fundamental problem for drug discovery and has been attracting growing attention.
Ranked #1 on
Molecular Graph Generation
on MOSES
AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks
Afterwards, a multi-head self-attentive neural network with residual connections is proposed to explicitly model the feature interactions in the low-dimensional space.
Ranked #4 on
Click-Through Rate Prediction
on KDD12
