Accelerating COVID-19 Research with GPUs
Skills:
Reading ML Papers70%
Key Takeaways
The video demonstrates how NVIDIA GPUs accelerate COVID-19 research by speeding up the screening of drug candidates using software like AutoDoc, which calculates the free energy of binding between candidate molecules and protein targets.
Full Transcript
the stakes are high as scientists and researchers are racing the clock to find a cure the search for an antiviral depends on finding the right chemical structure that binds to the viral proteins like the RNA polymerase shown here and that prevents the virus from attaching or replicating to screen drug candidates researchers estimate the free energy of binding between the candidate molecule and the protein target using software like Auto doc the binding free energy is a measure of the effective strength of the interaction between the ligand and the protein and is a good lead indicator of activity calculating the free energy requires evaluating the pairwise interim ik potential between atoms of the potential drug and the protein target millions of times as the drug molecule is rotated translated bonds rotated and protein side chains moved exploring the huge conformational space in search of just the right fit producing the most favorable configuration for binding but the combinatorics is daunting even the largest chemical databases which contain billions of compounds only represent a tiny fraction of the number of possible small molecules any one of which may hold the cure this means scientists must screen billions of molecules searching for just the right activity here's a visual representation of screening a database of compounds when using a cpu evaluating millions of poses for billions of molecules would take even a supercomputer years on a single GPU this can be done more than 230 times faster than on a single CPU thread using all twenty seven thousand six hundred and forty eight GPUs of Summit researchers are able to screen more than 25,000 molecules per second allowing them to dock 1 billion compounds in less than 12 hours all told on each node of Summit auto-doc running six GPUs is more than 33 times faster than if it were running on all 44 CPU cores in the node the race to find a cure is on and GPUs are accelerating the scientific computing that's moving us closer to finding it
Original Description
The stakes are high as scientists and researchers are racing against the clock to find a cure for COVID-19. The search for an antiviral depends on finding the right chemical structure that binds to the virus and interferences with its viral replication. Finding the right drug requires screening billions of drug candidates and identifying which drugs will most favorably bind with the virus. However, calculating the binding potential is computationally intensive and will take years to screen a billion compounds on a CPU system. The GPU accelerates this process by 33x and GPU-powered Summit system can now scan over 1B compounds in 12 hours.
Learn more: https://nvda.ws/37JFnjX
Watch on YouTube ↗
(saves to browser)
Sign in to unlock AI tutor explanation · ⚡30
Playlist
Uploads from NVIDIA Developer · NVIDIA Developer · 19 of 60
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
▶
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
Ray Tracing Essentials Part 2: Rasterization versus Ray Tracing
NVIDIA Developer
Ray Tracing Essentials Part 3: Ray Tracing Hardware
NVIDIA Developer
Ray Tracing Essentials Part 4: The Ray Tracing Pipeline
NVIDIA Developer
NsightGraphics 2020 2 Release Spotlight
NVIDIA Developer
Ray Tracing Essentials Part 5: Ray Tracing Effects
NVIDIA Developer
Ray Tracing Essentials Part 6: The Rendering Equation
NVIDIA Developer
Ray Tracing Essentials Part 7: Denoising for Ray Tracing
NVIDIA Developer
Spatiotemporal Importance Resampling for Many-Light Ray Tracing (ReSTIR)
NVIDIA Developer
Announcing Cloud-Native Support for Jetson Platform
NVIDIA Developer
JetsonTV: Build your next project with NVIDIA Jetson
NVIDIA Developer
Nsight Compute Feature Spotlight: Roofline Analysis, Asynchronous Copy, Sparse Data Compression
NVIDIA Developer
Nsight Systems Feature Spotlight: OpenMP
NVIDIA Developer
Isaac Sim 2020: Deep Dive
NVIDIA Developer
NVIDIA Jetson: Enabling AI-Powered Autonomous Machines at Scale
NVIDIA Developer
NVIDIA Tools to Train, Build, and Deploy Intelligent Vision Applications at the Edge
NVIDIA Developer
Jetson Xavier NX Developer Kit: The Next Leap in Edge Computing
NVIDIA Developer
Synthesizing High-Resolution Images with StyleGAN2
NVIDIA Developer
NVIDIA Robotics: Isaac SDK and Sim 2020.1
NVIDIA Developer
Accelerating COVID-19 Research with GPUs
NVIDIA Developer
Visualizing 150 Terabytes of Data
NVIDIA Developer
Boosting Performance and Utilization with Multi-Instance GPU
NVIDIA Developer
Running Multiple Workloads on a Single A100 GPU
NVIDIA Developer
NVIDIA Nsight Feature Spotlight: GPU Trace
NVIDIA Developer
Spark 3 Demo: Comparing Performance of GPUs vs. CPUs
NVIDIA Developer
NVIDIA Jetson Nano Wins Edge AI and Vision Alliance Award
NVIDIA Developer
NVIDIA IndeX on Google Cloud Platform Marketplace
NVIDIA Developer
DeepStream SDK: Best practices for performance optimization
NVIDIA Developer
Efficiently Deploying GPU Accelerated 5G CloudRAN for Edge AI Inferencing
NVIDIA Developer
NVIDIA PhysicsNeMo - Accelerating Scientific & Engineering Simulation Workflows with AI
NVIDIA Developer
NVIDIA Deep Learning Institute Instructor-Led Training Available Remotely
NVIDIA Developer
Advancing AR Glasses
NVIDIA Developer
Blender Cycles: RTX On
NVIDIA Developer
Real-Time GPU-Accelerated Data Analytics of 250 million Flight Data Records of 737 Max grounding
NVIDIA Developer
Assessing Property Damage with AI
NVIDIA Developer
RAPIDS: GPU-Accelerated Data Analytics & Machine Learning
NVIDIA Developer
DaVinci Resolve Turns RTX On
NVIDIA Developer
RAPIDS with Plotly Dash : GPU-Accelerated Census 2010 Visualization
NVIDIA Developer
NVIDIA IndeX for arivis5D Cloud Platform
NVIDIA Developer
NVIDIA Backchannel: Behind the Scenes of Marbles at Night RTX
NVIDIA Developer
NVIDIA Backchannel: Sneak Peek into Marbles RTX in Omniverse
NVIDIA Developer
How to Create "Paint" in Substance Painter
NVIDIA Developer
Accelerate AI development for Computer Vision on the NVIDIA Jetson with alwaysAI
NVIDIA Developer
Securing Next Generation Apps over VMware Cloud Foundation with Bluefield-2 DPU
NVIDIA Developer
Accelerated Data Centers with NVIDIA and VMware
NVIDIA Developer
GPU-Accelerated Motion Blur in Blender Cycles
NVIDIA Developer
NVIDIA Clara Guardian Virtual Patient Assistant
NVIDIA Developer
Revolutionizing Supercomputing with NVIDIA UFM Cyber-AI
NVIDIA Developer
Inventing Virtual Meetings of Tomorrow with NVIDIA AI Research
NVIDIA Developer
Learning a Contact-Adaptive Controller for Robust, Efficient Legged Locomotion
NVIDIA Developer
Getting started with Jetson Nano 2GB Developer Kit
NVIDIA Developer
NVIDIA Jetson Developer Community AI Projects
NVIDIA Developer
Open-source projects on NVIDIA Jetson Nano 2GB Developer Kit
NVIDIA Developer
Real-Time Ray Tracing with Project Lavina
NVIDIA Developer
Jetson AI Fundamentals - S1E2 - Hello Camera
NVIDIA Developer
Develop Optimized Conversational AI Models with NVIDIA NeMo on DGX A100
NVIDIA Developer
Jetson AI Fundamentals - S1E4 - Image Regression Project
NVIDIA Developer
Jetson AI Fundamentals - S2E1 - JetBot Intro and Hardware
NVIDIA Developer
Jetson AI Fundamentals - S2E2 - JetBot Software Setup
NVIDIA Developer
Jetson AI Fundamentals - S1E1 - First Time Setup with JetPack
NVIDIA Developer
Jetson AI Fundamentals - S1E3 - Image Classification Project
NVIDIA Developer
More on: Reading ML Papers
View skill →Related Reads
📰
📰
📰
📰
On July 1, 2026, arXiv will spin out from Cornell University, its home for the past 25 years, to become an independent nonprofit organization. Major funding support from Simons Foundation and Schmidt Sciences. Ditching the red for their website. [N]
Reddit r/MachineLearning
CS-NRRM™ Official Publications: Paper 1 and Paper 2 Are Now Available
Medium · Data Science
Found a potential mistake in an ICLR 2026 blogpost [D]
Reddit r/MachineLearning
Rebuttals Move Peer-Review Scores, but Initial-Review Structure Bounds the Movement
ArXiv cs.AI
🎓
Tutor Explanation
DeepCamp AI