deepmind

ConspectusMolecular docking, also termed ligand docking (LD), is a pivotal element of structure-based virtual screening (SBVS) used to predict the binding conformations and affinities of protein-ligand complexes. Traditional LD methodologies rely on a search and scoring framework, utilizing heuristic algorithms to explore binding conformations and scoring functions to evaluate binding strengths. However, to meet the efficiency demands of SBVS, these algorithms and functions are often simplified, prioritizing speed over accuracy...

The term "artificial general intelligence" (AGI) has become ubiquitous in current discourse around AI. OpenAI states that its mission is "to ensure that artificial general intelligence benefits all of humanity." DeepMind's company vision statement notes that "artificial general intelligence…has the potential to drive one of the greatest transformations in history." AGI is mentioned prominently in the UK government's National AI Strategy and in US government AI documents. Microsoft researchers recently claimed evidence of "sparks of AGI" in the large language model GPT-4, and current and former Google executives proclaimed that "AGI is already here...

Vacancy-ordered 12H-type hexagonal perovskites Ba6 Ru2 Na2 X2 O17 (X = P, V) with a ( c ' cchcc )2 stacking sequence of [BaO3 ] c , [BaO3 ] h , and [BaO2 ] c ' layers, where c and h represent a cubic and hexagonal stacking sequence, were previously reported by Quarez et al. in 2003. They also synthesized Ba6 Ta2 Na2 V2 O17 , but structural refinement was absent. Very recently, Szymanski et al. reported 43 new compounds, including 12H-type Ba6 Ta2 Na2 V2 O17 , using large-scale ab initio phase-stability data from the Materials Project and Google DeepMind with the assistance of an autonomous laboratory...

No abstract text is available yet for this article.

Functional bacterial amyloids play a crucial role in the formation of biofilms, which mediate chronic infections and contribute to antimicrobial resistance. This study focuses on the FapC amyloid fibrillar protein from Pseudomonas, a major contributor to biofilm formation. We investigate the initial steps of FapC amyloid formation and the impact of the chaperone-like protein FapA on this process. Using solution nuclear magnetic resonance (NMR), we recently showed that both FapC and FapA are intrinsically disordered proteins (IDPs)...

CONTEXT: With the union of machine learning (ML) and quantum chemistry, amid the debate between machine-learned functionals and human-designed functionals in density functional theory (DFT), this paper aims to demonstrate the generation of potential energy surfaces using computations with machine-learned density functional approximation (ML-DFA). A recent research trend is the application of ML in quantum sciences in the design of density functionals such as DeepMind's Deep Learning model (DeepMind21, DM21)...

The recent release of ChatGPT, a chat bot research project/product of natural language processing (NLP) by OpenAI, stirs up a sensation among both the general public and medical professionals, amassing a phenomenally large user base in a short time. This is a typical example of the 'productization' of cutting-edge technologies, which allows the general public without a technical background to gain firsthand experience in artificial intelligence (AI), similar to the AI hype created by AlphaGo (DeepMind Technologies, UK) and self-driving cars (Google, Tesla, etc...

No abstract text is available yet for this article.

Artificial intelligence has made significant advances in the field of protein structure prediction in recent years. In particular, DeepMind's end-to-end model, AlphaFold2, has demonstrated the capability to predict three-dimensional structures of numerous unknown proteins with accuracy levels comparable to those of experimental methods. This breakthrough has opened up new possibilities for understanding protein structure and function as well as accelerating drug discovery and other applications in the field of biology and medicine...

Most sequence variants encountered in medical genetics are of unknown significance, and their interpretation is a major stumbling block. Building on the successful AlphaFold system, the DeepMind group at Google has built a tool that predicts the pathogenic potential of any substitution in the human proteome. This is a major achievement and will be an important asset in clinical genetics.

No abstract text is available yet for this article.

Many proteins contain cleavable signal or transit peptides that direct them to their final subcellular locations. Such peptides are usually predicted from sequence alone using methods such as TargetP 2.0 and SignalP 6.0. While these methods are usually very accurate, we show here that an analysis of a protein's AlphaFold2-predicted structure can often be used to identify false positive predictions. We start by showing that when given a protein's full-length sequence, AlphaFold2 builds experimentally annotated signal and transit peptides in orientations that point away from the main body of the protein...

AI-powered discovery could lead to revolutions in electronics, batteries, and solar cells.

To close the gap between the rates of computational screening and experimental realization of novel materials1,2 , we introduce the A-Lab, an autonomous laboratory for the solid-state synthesis of inorganic powders. This platform uses computations, historical data from the literature, machine learning (ML) and active learning to plan and interpret the outcomes of experiments performed using robotics. Over 17 days of continuous operation, the A-Lab realized 41 novel compounds from a set of 58 targets including a variety of oxides and phosphates that were identified using large-scale ab initio phase-stability data from the Materials Project and Google DeepMind...

World models learn the consequences of actions in vision-based interactive systems. However, in practical scenarios like autonomous driving, noncontrollable dynamics that are independent or sparsely dependent on action signals often exist, making it challenging to learn effective world models. To address this issue, we propose Iso-Dream++, a model-based reinforcement learning approach that has two main contributions. First, we optimize the inverse dynamics to encourage the world model to isolate controllable state transitions from the mixed spatiotemporal variations of the environment...

No abstract text is available yet for this article.

In CASP15, 87 predictors submitted around 11 000 models on 41 assembly targets. The community demonstrated exceptional performance in overall fold and interface contact predictions, achieving an impressive success rate of 90% (compared to 31% in CASP14). This remarkable accomplishment is largely due to the incorporation of DeepMind's AF2-Multimer approach into custom-built prediction pipelines. To evaluate the added value of participating methods, we compared the community models to the baseline AF2-Multimer predictor...

MOTIVATION: In recent years, there has been a breakthrough in protein structure prediction, and the AlphaFold2 model of the DeepMind team has improved the accuracy of protein structure prediction to the atomic level. Currently, deep learning-based protein function prediction models usually extract features from protein sequences and combine them with protein-protein interaction networks to achieve good results. However, for newly sequenced proteins that are not in the protein-protein interaction network, such models cannot make effective predictions...

This year's Lasker Basic Science Award recognizes the invention of AlphaFold, a revolutionary advance in the history of protein research which for the first time offers the practical ability to accurately predict the three-dimensional arrangement of amino acids in the vast majority of proteins on a genomic scale on the basis of sequence alone [J. Jumper et al. , Nature 596 , 583-589 (2021) and K. Tunyasuvunakool et al., Nature 596 , 590-596 (2021)]. This extraordinary achievement by Demis Hassabis and John Jumper and their coworkers at Google's DeepMind and other collaborators was built on decades of experimental protein structure determination (structural biology) as well as the gradual development of multiple strategies incorporating biologically inspired statistical approaches...

When the results of DeepMind's AlphaFold2 at CASP were announced in 2020, the scientific world was so amazed by how effectively it performed that "it will change everything" became the motto for this revolution [...].