Protein Sequence Encoder

Protein sequence encoders are computational tools designed to represent protein sequences as numerical vectors, facilitating analysis and prediction of protein properties and functions. Current research focuses on developing sophisticated encoder architectures, including transformers and graph neural networks, often incorporating contrastive learning and multimodal data integration (e.g., combining sequence and structural information) to improve accuracy and efficiency. These advancements are significantly impacting various fields, such as drug discovery (via virtual screening and de novo molecule design) and protein engineering (through improved mutation analysis and design), by enabling faster and more accurate predictions based on protein sequence data.

Papers

August 31, 2023

BioCoder: A Benchmark for Bioinformatics Code Generation with Large Language Models
Xiangru Tang, Bill Qian, Rick Gao, Jiakang Chen, Xinyun Chen, Mark Gerstein
Large Language Model New Benchmark Real World Code Protein Sequence Encoder

February 4, 2023

Implicit Geometry and Interaction Embeddings Improve Few-Shot Molecular Property Prediction
Christopher Fifty, Joseph M. Paggi, Ehsan Amid, Jure Leskovec, Ron Dror
Molecular Property Prediction Protein Sequence Encoder Interaction Representation Molecular Embeddings Shot Molecular Property Prediction

January 28, 2023

Pre-Training Protein Encoder via Siamese Sequence-Structure Diffusion Trajectory Prediction
Zuobai Zhang, Minghao Xu, Aurélie Lozano, Vijil Chenthamarakshan, Payel Das, Jian Tang
Siamese Network Protein Sequence Encoder Pre Trained Protein Model Sequence Diffusion

October 19, 2022

An efficient graph generative model for navigating ultra-large combinatorial synthesis libraries
Aryan Pedawi, Pawel Gniewek, Chaoyi Chang, Brandon M. Anderson, Henry van den Bedem
Protein Sequence Encoder Compound Library Variational Encoder Efficient Graph Synthesis Algorithm

May 2, 2022

Attention-wise masked graph contrastive learning for predicting molecular property
Hui Liu, Yibiao Huang, Xuejun Liu, Lei Deng
Contrastive Learning Graph Drawing Molecular Representation Molecular Property Molecular Property Prediction Task Protein Sequence Encoder Pay Attention

March 11, 2022

Protein Representation Learning by Geometric Structure Pretraining
Zuobai Zhang, Minghao Xu, Arian Jamasb, Vijil Chenthamarakshan, Aurelie Lozano, Payel Das, Jian Tang
Protein Language Model Protein Sequence Encoder Protein Representation Learning Pre Trained Protein Model

February 23, 2022

Using Deep Learning to Detect Digitally Encoded DNA Trigger for Trojan Malware in Bio-Cyber Attacks
Mohd Siblee Islam, Stepan Ivanov, Hamdan Awan, Jennifer Drohan, Sasitharan Balasubramaniam, Lee Coffey, Srivatsan Kidambi, Witty Sri-saan
Deep Learning Cyber Attack Trojan Attack Protein Sequence Encoder Hybrid Attack

January 24, 2022

ReLSO: A Transformer-based Model for Latent Space Optimization and Generation of Proteins
Egbert Castro, Abhinav Godavarthi, Julian Rubinfien, Kevin B. Givechian, Dhananjay Bhaskar, Smita Krishnaswamy
Faithful Generation Transformer Based Model Non Structural Protein Latent Space Optimization Protein Sequence Encoder Fitness Approximation Sequence Optimization

Protein Sequence Encoder

Papers

BioCoder: A Benchmark for Bioinformatics Code Generation with Large Language Models

Implicit Geometry and Interaction Embeddings Improve Few-Shot Molecular Property Prediction

Pre-Training Protein Encoder via Siamese Sequence-Structure Diffusion Trajectory Prediction

An efficient graph generative model for navigating ultra-large combinatorial synthesis libraries

Attention-wise masked graph contrastive learning for predicting molecular property

Protein Representation Learning by Geometric Structure Pretraining

Using Deep Learning to Detect Digitally Encoded DNA Trigger for Trojan Malware in Bio-Cyber Attacks

ReLSO: A Transformer-based Model for Latent Space Optimization and Generation of Proteins