Scientists at Google DeepMind have built an artificial intelligence program that can predict whether millions of genetic mutations are either harmless or likely to cause disease, in an effort to speed up research and the diagnosis of rare disorders. The program makes predictions about so-called missense mutations, where a single letter is misspelt in the DNA code. Such mutations are often harmless but they can disrupt how proteins work and cause diseases from cystic fibrosis and sickle-cell anaemia to cancer and problems with brain development. The researchers used AlphaMissense to assess all 71m single-letter mutations that could affect human proteins. When they set the program's precision to 90%, it predicted that 57% of missense mutations were probably harmless and 32% were probably harmful. It was uncertain about the impact of the rest.

Biologists inspired by digital art generators like DALL-E decide to build artificial intelligence human proteins that can fight cancer, flu, and Covid. DALL-E works by processing the text descriptions through several layers of neural networks, which are sets of algorithms that are designed to mimic the way the human brain works. These neural networks analyze the text and extract a representation of the image that is described. This representation is then used to generate the new image, which is done by passing it through a decoder network. The decoder network then generates a new image that corresponds to the text description. One of the key features of DALL-E is its ability to generate images that are not present in the training dataset.

Designing drugs that target proteins requires knowing the three-dimensional (3D) shape of the target protein and creating a drug whose shape binds to that target protein. As of June 2021, research had only determined the shape of one-third of human proteins. Then, in July 2021, two groups used artificial intelligence (AI) to predict the shape of nearly every human protein from nucleic acid sequences. The same AI software that predicted the shape of proteins in the 2021 experiments has now been used to design small strings of 50 to 65 amino acids ("mini-antibodies") with 3D shapes that were predicted to bind to 13 important human target proteins: growth factors that are important in stimulating the growth of cancers and components of several infectious agents, including influenza and SARS-CoV-2. The mini-antibodies were shown to bind to their targets and to work as expected--for example, a mini-antibody designed to bind to the SARS-CoV-2 spike protein protected mice from infection.

Marcelo Der Torossian Torres lifted the clear plastic cover off of a petri dish one morning last June. The dish, still warm from its sleepover in the incubator, smelled of rancid broth. Inside it sat a rubbery bed of amber-colored agar, and on that bed lay neat rows of pinpricks--dozens of colonies of drug-resistant bacteria sampled from the skin of a lab mouse. Torres counted each pinprick softly to himself, then did some quick calculations. Untreated for the infection, the samples taken from an abscess on the mouse had yielded billions of superbugs, or antibiotic-resistant bacteria.

Advances in biomedicine are largely fueled by exploring uncharted territories of human biology. Machine learning can both enable and accelerate discovery, but faces a fundamental hurdle when applied to unseen data with distributions that differ from previously observed ones -- a common dilemma in scientific inquiry. We have developed a new deep learning framework, called {\textit{Portal Learning}}, to explore dark chemical and biological space. Three key, novel components of our approach include: (i) end-to-end, step-wise transfer learning, in recognition of biology's sequence-structure-function paradigm, (ii) out-of-cluster meta-learning, and (iii) stress model selection. Portal Learning provides a practical solution to the out-of-distribution (OOD) problem in statistical machine learning. Here, we have implemented Portal Learning to predict chemical-protein interactions on a genome-wide scale. Systematic studies demonstrate that Portal Learning can effectively assign ligands to unexplored gene families (unknown functions), versus existing state-of-the-art methods, thereby allowing us to target previously "undruggable" proteins and design novel polypharmacological agents for disrupting interactions between SARS-CoV-2 and human proteins. Portal Learning is general-purpose and can be further applied to other areas of scientific inquiry.

Last month, DeepMind published the much anticipated, detailed methodology underlying the latest version of AlphaFold – the UK-based science company's powerful AI system that blew away its rivals in the latest major competition to predict the 3D structure of proteins. AlphaFold's machine learning methodology has been applied to predict structures for almost 99% of human proteins which have now been made publicly available. In this long read, I reflect on the significance of these developments for fundamental research and drug discovery. I wrote this as the ICR celebrates the 10th anniversary of its AI-enabled drug discovery knowledgebase canSAR – which features multiple approaches to predicting'druggability' as an aid to selecting drug targets and accelerating drug discovery. The coronavirus pandemic has, understandably, soaked up a lot of bandwidth when it comes to science news – but one particular non-Covid science story was able to cut through and hit the headlines in the UK and around the world. On 30 November 2020 it was announced that DeepMind – a subsidiary of Google's parent company Alphabet focusing on artificial intelligence – had made what was hailed as a huge leap towards solving one of biology's greatest remaining challenges: the ability to predict the correct, three-dimensional structures of proteins based on their constituent, one-dimensional amino acid sequences. The announcement attracted huge interest, but the expert community has been waiting for the peer-reviewed science publication. The AI methodology has now been published in the leading journal Nature and this was followed rapidly by a second Nature paper from DeepMind and collaborators at the European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), which reports the application of the most recent AlphaFold machine learning system to predict the 3D structures at scale for almost the entire human proteome – 98.5% of human proteins.

AI research lab DeepMind has created the most comprehensive map of human proteins to date using artificial intelligence. The company, a subsidiary of Google-parent Alphabet, is releasing the data for free, with some scientists comparing the potential impact of the work to that of the Human Genome Project, an international effort to map every human gene. Proteins are long, complex molecules that perform numerous tasks in the body, from building tissue to fighting disease. Their purpose is dictated by their structure, which folds like origami into complex and irregular shapes. Understanding how a protein folds helps explain its function, which in turn helps scientists with a range of tasks -- from pursuing fundamental research on how the body works, to designing new medicines and treatments.

It took decades of painstaking research to map the structure of just 17 per cent of the proteins used within the human body, but less than a year for UK-based AI company DeepMind to raise that figure to 98.5 per cent. The company is making all this data freely available, which could lead to rapid advances in the development of new drugs. Determining the complex, crumpled shape of proteins based on the sequence of amino acids that make them has been a huge scientific hurdle. Some amino acids are attracted to others, some are repelled by water, and the chains form intricate shapes that are hard to calculate accurately. Understanding these structures enables new, highly targeted drugs to be designed that bind to specific parts of proteins. Genetic research had long provided the ability to determine the sequence of a protein, but an efficient way of finding the shape – crucial to understanding its properties – has proven elusive.

Currently, there is a rapidly increasing need for high-quality biomedical knowledge graphs (BioKG) that provide direct and precise biomedical knowledge. In the context of COVID-19, this issue is even more necessary to be highlighted. However, most BioKG construction inevitably includes numerous conflicts and noises deriving from incorrect knowledge descriptions in literature and defective information extraction techniques. Many studies have demonstrated that reasoning upon the knowledge graph is effective in eliminating such conflicts and noises. This paper proposes a method BioGRER to improve the BioKG's quality, which comprehensively combines the knowledge graph embedding and logic rules that support and negate triplets in the BioKG. In the proposed model, the BioKG refinement problem is formulated as the probability estimation for triplets in the BioKG. We employ the variational EM algorithm to optimize knowledge graph embedding and logic rule inference alternately. In this way, our model could combine efforts from both the knowledge graph embedding and logic rules, leading to better results than using them alone. We evaluate our model over a COVID-19 knowledge graph and obtain competitive results.

A drug screen using machine learning has identified hundreds of potential drugs that could be used to treat COVID-19, researchers say. Researchers have used machine learning to identify hundreds of new potential drugs that could help treat COVID-19, the disease caused by SARS-CoV-2. The study was conducted at the University of California, Riverside, US. "There is an urgent need to identify effective drugs that treat or prevent COVID-19," said Professor Anandasankar Ray, who led the research. "We have developed a drug discovery pipeline that identified several candidates… Existing US Food and Drug Administration (FDA)-approved drugs that target one or more human proteins important for viral entry and replication are currently high priority for repurposing as new COVID-19 drugs. The demand is high for additional drugs or small molecules that can interfere with both entry and replication of SARS-CoV-2 in the body. Our drug discovery pipeline can help."