A5050109164- Protein Structure and Dynamics
- Machine Learning in Bioinformatics
- Enzyme Structure and Function
- Gaussian Processes and Bayesian Inference
- Machine Learning in Materials Science
- Neural Networks and Applications
- Model Reduction and Neural Networks
- Reinforcement Learning in Robotics
- Artificial Intelligence in Games
- Advanced Chemical Physics Studies
- Memory and Neural Mechanisms
- Digital Games and Media
- Evolutionary Algorithms and Applications
- Stochastic Gradient Optimization Techniques
- Speech and Audio Processing
- Underwater Acoustics Research
- Neural and Behavioral Psychology Studies
- Acoustic Wave Phenomena Research
- Spectroscopy and Quantum Chemical Studies
- Face and Expression Recognition
- Robotic Path Planning Algorithms
- Anomaly Detection Techniques and Applications
- Robotics and Sensor-Based Localization
- Plant biochemistry and biosynthesis
- Neural dynamics and brain function
DeepMind (United Kingdom)
2018-2022
Google (United States)
2015-2018
Abstract Proteins are essential to life, and understanding their structure can facilitate a mechanistic of function. Through an enormous experimental effort 1–4 , the structures around 100,000 unique proteins have been determined 5 but this represents small fraction billions known protein sequences 6,7 . Structural coverage is bottlenecked by months years painstaking required determine single structure. Accurate computational approaches needed address gap enable large-scale structural...
The AlphaFold Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk) is an openly accessible, extensive database of high-accuracy protein-structure predictions. Powered by v2.0 DeepMind, it has enabled unprecedented expansion the structural coverage known protein-sequence space. DB provides programmatic access to and interactive visualization predicted atomic coordinates, per-residue pairwise model-confidence estimates aligned errors. initial release contains over 360,000...
Abstract Protein structures can provide invaluable information, both for reasoning about biological processes and enabling interventions such as structure-based drug development or targeted mutagenesis. After decades of effort, 17% the total residues in human protein sequences are covered by an experimentally determined structure 1 . Here we markedly expand structural coverage proteome applying state-of-the-art machine learning method, AlphaFold 2 , at a scale that covers almost entire...
This paper introduces SC2LE (StarCraft II Learning Environment), a reinforcement learning environment based on the StarCraft game. domain poses new grand challenge for learning, representing more difficult class of problems than considered in most prior work. It is multi-agent problem with multiple players interacting; there imperfect information due to partially observed map; it has large action space involving selection and control hundreds units; state that must be solely from raw input...
We present the first massively distributed architecture for deep reinforcement learning. This uses four main components: parallel actors that generate new behaviour; learners are trained from stored experience; a neural network to represent value function or behaviour policy; and store of experience. used our implement Deep Q-Network algorithm (DQN). Our was applied 49 games Atari 2600 Arcade Learning Environment, using identical hyperparameters. performance surpassed non-distributed DQN in...
Abstract We describe AlphaFold, the protein structure prediction system that was entered by group A7D in CASP13. Submissions were made three free‐modeling (FM) methods which combine predictions of neural networks. All systems guided distances between pairs residues produced a network. Two assembled fragments generative network, one using scores from network trained to regress GDT_TS. The third shows simple gradient descent on properly constructed potential is able perform par with more...
We describe the operation and improvement of AlphaFold, system that was entered by team AlphaFold2 to "human" category in 14th Critical Assessment Protein Structure Prediction (CASP14). The AlphaFold CASP14 is entirely different one CASP13. It used a novel end-to-end deep neural network trained produce protein structures from amino acid sequence, multiple sequence alignments, homologous proteins. In assessors' ranking summed z scores (>2.0), scored 244.0 compared 90.8 next best group....
Density functional theory describes matter at the quantum level, but all popular approximations suffer from systematic errors that arise violation of mathematical properties exact functional. We overcame this fundamental limitation by training a neural network on molecular data and fictitious systems with fractional charge spin. The resulting functional, DM21 (DeepMind 21), correctly typical examples artificial delocalization strong correlation performs better than traditional functionals...
We present Spectral Inference Networks, a framework for learning eigenfunctions of linear operators by stochastic optimization. Networks generalize Slow Feature Analysis to generic symmetric operators, and are closely related Variational Monte Carlo methods from computational physics. As such, they can be powerful tool unsupervised representation video or graph-structured data. cast training as bilevel optimization problem, which allows online multiple eigenfunctions. show results on...