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Found 16 papers, 12 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.
ProtIR: Iterative Refinement between Retrievers and Predictors for Protein Function Annotation
Protein function annotation is an important yet challenging task in biology.
Structure-Informed Protein Language Model
To address this issue, we introduce the integration of remote homology detection to distill structural information into protein language models without requiring explicit protein structures as input.
PDB-Struct: A Comprehensive Benchmark for Structure-based Protein Design
Structure-based protein design has attracted increasing interest, with numerous methods being introduced in recent years.
Str2Str: A Score-based Framework for Zero-shot Protein Conformation Sampling
The dynamic nature of proteins is crucial for determining their biological functions and properties, for which Monte Carlo (MC) and molecular dynamics (MD) simulations stand as predominant tools to study such phenomena.
DiffPack: A Torsional Diffusion Model for Autoregressive Protein Side-Chain Packing
In this work, we present DiffPack, a torsional diffusion model that learns the joint distribution of side-chain torsional angles, the only degrees of freedom in side-chain packing, by diffusing and denoising on the torsional space.
A Systematic Study of Joint Representation Learning on Protein Sequences and Structures
Recent sequence representation learning methods based on Protein Language Models (PLMs) excel in sequence-based tasks, but their direct adaptation to tasks involving protein structures remains a challenge.
FusionRetro: Molecule Representation Fusion via In-Context Learning for Retrosynthetic Planning
Current strategies use a decoupled approach of single-step retrosynthesis models and search algorithms, taking only the product as the input to predict the reactants for each planning step and ignoring valuable context information along the synthetic route.
PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence Understanding
However, there is a lack of a standard benchmark to evaluate the performance of different methods, which hinders the progress of deep learning in this field.
Neural-Symbolic Models for Logical Queries on Knowledge Graphs
Answering complex first-order logic (FOL) queries on knowledge graphs is a fundamental task for multi-hop reasoning.
Ranked #3 on
Complex Query Answering
on FB15k-237
A Roadmap for Big Model
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm.
Protein Representation Learning by Geometric Structure Pretraining
Despite the effectiveness of sequence-based approaches, the power of pretraining on known protein structures, which are available in smaller numbers only, has not been explored for protein property prediction, though protein structures are known to be determinants of protein function.
Structured Multi-task Learning for Molecular Property Prediction
However, in contrast to other domains, the performance of multi-task learning in drug discovery is still not satisfying as the number of labeled data for each task is too limited, which calls for additional data to complement the data scarcity.
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.
Multi-task Learning with Domain Knowledge for Molecular Property Prediction
In this paper, we study multi-task learning for molecule property prediction in a different setting, where a relation graph between different tasks is available.
Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction
To further improve the capacity of the path formulation, we propose the Neural Bellman-Ford Network (NBFNet), a general graph neural network framework that solves the path formulation with learned operators in the generalized Bellman-Ford algorithm.
Ranked #1 on
Link Prediction
on FB15k-237
