Search Results for author:
Found 11 papers, 5 papers with code
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.
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.
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-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).
Coupling streaming AI and HPC ensembles to achieve 100-1000x faster biomolecular simulations
The use of ML methods to dynamically steer ensemble-based simulations promises significant improvements in the performance of scientific applications.
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.
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.
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.
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).
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.
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.
