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Found 19 papers, 9 papers with code
Hierarchical Convolutional Attention Networks for Text Classification
Recent work in machine translation has demonstrated that self-attention mechanisms can be used in place of recurrent neural networks to increase training speed without sacrificing model accuracy.
DeepDriveMD: Deep-Learning Driven Adaptive Molecular Simulations for Protein Folding
Our study provides a quantitative basis to understand how DL driven MD simulations, can lead to effective performance gains and reduced times to solution on supercomputing resources.
Targeting SARS-CoV-2 with AI- and HPC-enabled Lead Generation: A First Data Release
Researchers across the globe are seeking to rapidly repurpose existing drugs or discover new drugs to counter the the novel coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).
Artificial intelligence techniques for integrative structural biology of intrinsically disordered proteins
We outline recent developments in artificial intelligence (AI) and machine learning (ML) techniques for integrative structural biology of intrinsically disordered proteins (IDP) ensembles.
Pandemic Drugs at Pandemic Speed: Infrastructure for Accelerating COVID-19 Drug Discovery with Hybrid Machine Learning- and Physics-based Simulations on High Performance Computers
The race to meet the challenges of the global pandemic has served as a reminder that the existing drug discovery process is expensive, inefficient and slow.
Scaffold Embeddings: Learning the Structure Spanned by Chemical Fragments, Scaffolds and Compounds
Molecules have seemed like a natural fit to deep learning's tendency to handle a complex structure through representation learning, given enough data.
Coupling streaming AI and HPC ensembles to achieve 100-1000x faster biomolecular simulations
The results establish DeepDriveMD as a high-performance framework for ML-driven HPC simulation scenarios, that supports diverse MD simulation and ML back-ends, and which enables new scientific insights by improving the length and time scales accessible with current computing capacity.
Protein-Ligand Docking Surrogate Models: A SARS-CoV-2 Benchmark for Deep Learning Accelerated Virtual Screening
Our analysis of the speedup explains that to screen more molecules under a docking paradigm, another order of magnitude speedup must come from model accuracy rather than computing speed (which, if increased, will not anymore alter our throughput to screen molecules).
CrossedWires: A Dataset of Syntactically Equivalent but Semantically Disparate Deep Learning Models
Currently, our ability to build standardized deep learning models is limited by the availability of a suite of neural network and corresponding training hyperparameter benchmarks that expose differences between existing deep learning frameworks.
Protein Folding Neural Networks Are Not Robust
We define the robustness measure for the predicted structure of a protein sequence to be the inverse of the root-mean-square distance (RMSD) in the predicted structure and the structure of its adversarially perturbed sequence.
Scaffold-Induced Molecular Graph (SIMG): Effective Graph Sampling Methods for High-Throughput Computational Drug Discovery
Scaffold based drug discovery (SBDD) is a technique for drug discovery which pins chemical scaffolds as the framework of design.
Deep Surrogate Docking: Accelerating Automated Drug Discovery with Graph Neural Networks
The process of screening molecules for desirable properties is a key step in several applications, ranging from drug discovery to material design.
On the Robustness of AlphaFold: A COVID-19 Case Study
To measure the robustness of the predicted structures, we utilize (i) the root-mean-square deviation (RMSD) and (ii) the Global Distance Test (GDT) similarity measure between the predicted structure of the original sequence and the structure of its adversarially perturbed version.
A Text-guided Protein Design Framework
Current AI-assisted protein design mainly utilizes protein sequential and structural information.
Causal Discovery and Optimal Experimental Design for Genome-Scale Biological Network Recovery
Causal discovery of genome-scale networks is important for identifying pathways from genes to observable traits - e. g. differences in cell function, disease, drug resistance and others.
Transferable Graph Neural Fingerprint Models for Quick Response to Future Bio-Threats
With this dataset, we trained graph neural fingerprint docking models for high-throughput virtual COVID-19 drug screening.
Linking the Dynamic PicoProbe Analytical Electron-Optical Beam Line / Microscope to Supercomputers
The Dynamic PicoProbe at Argonne National Laboratory is undergoing upgrades that will enable it to produce up to 100s of GB of data per day.
DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies
In the upcoming decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences.
Equivariant Graph Neural Operator for Modeling 3D Dynamics
Modeling the complex three-dimensional (3D) dynamics of relational systems is an important problem in the natural sciences, with applications ranging from molecular simulations to particle mechanics.
