866 citations · 1.8k across the 14 of their papers we have counts for
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Cross-Modality Protein Embedding for Compound-Protein Affinity and Contact Prediction
Yuning You, Yang Shen
Compound-protein pairs dominate FDA-approved drug-target pairs and the prediction of compound-protein affinity and contact (CPAC) could help accelerate drug discovery. In this stud…
Explainable Deep Relational Networks for Predicting Compound-Protein Affinities and Contacts
Mostafa Karimi, Di Wu, Zhangyang Wang +1
Predicting compound-protein affinity is critical for accelerating drug discovery. Recent progress made by machine learning focuses on accuracy but leaves much to be desired for int…
Energy-based Graph Convolutional Networks for Scoring Protein Docking Models
Yue Cao, Yang Shen
Structural information about protein-protein interactions, often missing at the interactome scale, is important for mechanistic understanding of cells and rational discovery of the…
Bayesian active learning for optimization and uncertainty quantification in protein docking
Yue Cao, Yang Shen
Motivation: Ab initio protein docking represents a major challenge for optimizing a noisy and costly "black box"-like function in a high-dimensional space. Despite progress in this…
DeepAffinity: Interpretable Deep Learning of Compound-Protein Affinity through Unified Recurrent and Convolutional Neural Networks
Mostafa Karimi, Di Wu, Zhangyang Wang +1
Motivation: Drug discovery demands rapid quantification of compound-protein interaction (CPI). However, there is a lack of methods that can predict compound-protein affinity from s…