15 citations · 32 across the 6 of their papers we have counts for
6 papers
QuickBind: A Light-Weight And Interpretable Molecular Docking Model
Wojtek Treyde, Seohyun Chris Kim, Nazim Bouatta +1
Predicting a ligand's bound pose to a target protein is a key component of early-stage computational drug discovery. Recent developments in machine learning methods have focused on…
DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies
Shuaiwen Leon Song, Bonnie Kruft, Minjia Zhang +89
In the upcoming decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences. This could herald a new era of scient…
Growing ecosystem of deep learning methods for modeling protein$\unicode{x2013}$protein interactions
Julia R. Rogers, Gergő Nikolényi, Mohammed AlQuraishi
Numerous cellular functions rely on protein$\unicode{x2013}$protein interactions. Efforts to comprehensively characterize them remain challenged however by the diversity of molecul…
OpenProteinSet: Training data for structural biology at scale
Gustaf Ahdritz, Nazim Bouatta, Sachin Kadyan +7
Multiple sequence alignments (MSAs) of proteins encode rich biological information and have been workhorses in bioinformatic methods for tasks like protein design and protein struc…
Accurate Protein Structure Prediction by Embeddings and Deep Learning Representations
Iddo Drori, Darshan Thaker, Arjun Srivatsa +15
Proteins are the major building blocks of life, and actuators of almost all chemical and biophysical events in living organisms. Their native structures in turn enable their biolog…
ProteinNet: a standardized data set for machine learning of protein structure
Mohammed AlQuraishi
Rapid progress in deep learning has spurred its application to bioinformatics problems including protein structure prediction and design. In classic machine learning problems like…