Forwarded from Graph Machine Learning
Generative modeling with proteins (hundreds of them either):
EquiJump: Protein Dynamics Simulation via SO(3)-Equivariant Stochastic Interpolants
Design of Ligand-Binding Proteins with Atomic Flow Matching
RapidDock: Unlocking Proteome-scale Molecular Docking
Deep Learning for Protein-Ligand Docking: Are We There Yet?
ProteinBench: A Holistic Evaluation of Protein Foundation Models
Fast and Accurate Blind Flexible Docking
Solving Inverse Problems in Protein Space Using Diffusion-Based Priors
Crystals and Materials:
Flow Matching for Accelerated Simulation of Atomic Transport in Materials
MOFFlow: Flow Matching for Structure Prediction of Metal-Organic Frameworks
Learning the Hamiltonian of Disordered Materials with Equivariant Graph Networks
Designing Mechanical Meta-Materials by Learning Equivariant Flows
SymmCD: Symmetry-Preserving Crystal Generation with Diffusion Models
Rethinking the role of frames for SE(3)-invariant crystal structure modeling
A Periodic Bayesian Flow for Material Generation
ECD: A Machine Learning Benchmark for Predicting Enhanced-Precision Electronic Charge Density in Crystalline Inorganic Materials
Wyckoff Transformer: Generation of Symmetric Crystals
PDDFormer: Pairwise Distance Distribution Graph Transformer for Crystal Material Property Prediction
EquiJump: Protein Dynamics Simulation via SO(3)-Equivariant Stochastic Interpolants
Design of Ligand-Binding Proteins with Atomic Flow Matching
RapidDock: Unlocking Proteome-scale Molecular Docking
Deep Learning for Protein-Ligand Docking: Are We There Yet?
ProteinBench: A Holistic Evaluation of Protein Foundation Models
Fast and Accurate Blind Flexible Docking
Solving Inverse Problems in Protein Space Using Diffusion-Based Priors
Crystals and Materials:
Flow Matching for Accelerated Simulation of Atomic Transport in Materials
MOFFlow: Flow Matching for Structure Prediction of Metal-Organic Frameworks
Learning the Hamiltonian of Disordered Materials with Equivariant Graph Networks
Designing Mechanical Meta-Materials by Learning Equivariant Flows
SymmCD: Symmetry-Preserving Crystal Generation with Diffusion Models
Rethinking the role of frames for SE(3)-invariant crystal structure modeling
A Periodic Bayesian Flow for Material Generation
ECD: A Machine Learning Benchmark for Predicting Enhanced-Precision Electronic Charge Density in Crystalline Inorganic Materials
Wyckoff Transformer: Generation of Symmetric Crystals
PDDFormer: Pairwise Distance Distribution Graph Transformer for Crystal Material Property Prediction
Forwarded from Data Mining & Machine learning (Hirah Tang)
Forwarded from Data Mining & Machine learning (Hirah Tang)
Quanta Magazine
How AI Revolutionized Protein Science, but Didn’t End It | Quanta Magazine
Three years ago, Google’s AlphaFold pulled off the biggest artificial intelligence breakthrough in science to date, accelerating molecular research and kindling deep questions about why we do science.
Forwarded from Data Mining & Machine learning (Hirah Tang)
Forwarded from 懒人的梦呓 (virusyu🅥)
推荐吴恩达的新书：《How to build your career in AI》，不需要技术门槛也能阅读，这本书提供了全方位的AI职业发展建议，包括AI基础技能需要什么，求职面试，如何打造作品集，怎么建立人脉网络等，如果你想往AI方面发展，很值得一读。
下载地址：https://info.deeplearning.ai/how-to-build-a-career-in-ai-book
下载地址：https://info.deeplearning.ai/how-to-build-a-career-in-ai-book
info.deeplearning.ai
How to Build Your Career in AI eBook - Andrew Ng Collected Insights
Get The How to Build Your Career in AI eBook By Andrew NG | Free download | an introductory book about starting and building a successful career in AI
Forwarded from Data Mining & Machine learning (Hirah Tang)
David Stutz
The Importance of Effectively Experimenting in an AI PhD • David Stutz
Engineering and running experiments are a key component of most PhDs in AI. While there are plenty of more theoretical topics that are often limited to smaller scale experimentation, the trend has definitely been to scale up models, datasets and experiments.…