866 citations · 1.8k across the 14 of their papers we have counts for
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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…
Learning to Optimize in Swarms
Yue Cao, Tianlong Chen, Zhangyang Wang +1
Learning to optimize has emerged as a powerful framework for various optimization and machine learning tasks. Current such "meta-optimizers" often learn in the space of continuous…
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…