Artificial intelligence (AI) and machine-learning (ML) approaches are the present-day buzzwords finding applications in a host of domains affecting our lives. These approaches use known datasets to train and build models that can predict, or sometimes, make decisions about a task. In one such case, researchers at the Indian Institute of Technology Bombay (IIT Bombay), Mumbai, have in a recent study, developed ML approaches using molecular descriptors for certain types of catalysis that could find use in several therapeutic applications. Traditionally, drug discovery and formulation is an elaborate process. Biological molecules have different properties, the knowledge of which are crucial for binding drug molecules that target proteins.
Tech these days is often accused of encouraging forms of addiction, but emerging "cyborg" technology may offer an answer for treating the opioid epidemic. Embedding microchips in the brains of addicts could help to, essentially, rewire them. He's among millions of people in America affected by what has become a national plague that kills hundreds each day. He hopes, though, that the computer chip in his brain can break him from addiction's hold. His dependence took hold after he dislocated his shoulder when he was 15.
Microorganisms perform key functions in ecosystems and their diversity reflects the health of their environment. However, they are still largely under-exploited in current biomonitoring programs because they are difficult to identify. Researchers from the University of Geneva (UNIGE), Switzerland, have recently developed an approach combining two cutting edge technologies to fill this gap. They use genomic tools to sequence the DNA of microorganisms in samples, and then exploit this considerable amount of data with artificial intelligence. They build predictive models capable of establishing a diagnosis of the health of ecosystems on a large scale and identify species that perform important functions.
Enzyme engineering plays a central role in developing efficient biocatalysts for biotechnology, biomedicine, and life sciences. Apart from classical rational design and directed evolution approaches, machine learning methods have been increasingly applied to find patterns in data that help predict protein structures, improve enzyme stability, solubility, and function, predict substrate specificity, and guide rational protein design. In this Perspective, we analyze the state of the art in databases and methods used for training and validating predictors in enzyme engineering. We discuss current limitations and challenges which the community is facing and recent advancements in experimental and theoretical methods that have the potential to address those challenges.
British company Exscientia has been working with several pharmaceutical companies (including Sanofi, GlaxoSmithKline, and Roche), offering its artificial intelligence system to aid the drug discovery process. With the new announcement, Bayer are to back the project with €240 million ($266 million) over the course of three years. The focus of this digital transformation of the medication development process will be on the application of artificial intelligence to speed up the discovery of small molecule drug candidates. The drug candidates will have targets linked to oncology and cardiovascular disease. The deal between the two companies, as PharmaPorum reports, will see Bayer owning the rights to the compounds and Exscientia will receive royalties relating to future sales.
With a dish of cells as a canvas, Anne Carpenter's collaborators apply layers of color. Each one highlights a different cellular feature: A fluorescent blue dye to stain the nuclei. Orange to label the cell membranes. This approach, called "Cell Painting," uses six biological dyes to stain eight major cell structures. Together, they create not just beautiful images, but also a detailed portrait of the cells' size, shape or morphology, and--if you can read the signs--physiological state.
Researchers at Purdue University have developed a new deep learning algorithm, called DOVE, that can improve modelling of proteins and help create new drugs. The human body contains over 20,000 different types of proteins, which interact with each other to enable life as we know it. Currently, protein docking models have been developed to estimate how two proteins will interact, yet it is challenging to score whether or not the predicted docking estimate is correct. The Purdue researchers developed a new computational method to address this challenge. DOVE, short for Docking decoy selection with Voxel-based deep neural nEtwork, first scans protein-protein interfaces of a proposed protein docking configuration using a 3D voxel, while considering the atomic interactions and energetic contributions.
The Pharmaceutical Bioinformatics research group focuses on mathematical and statistical modeling, informatics and quantitative analysis of pharmacological systems. We develop methods, algorithms and software to study and model pharmaceutical interactions, and a key focus in the group is how artificial intelligence (AI) and machine learning can aid the drug discovery process; e.g. in drug screening and when studying drug toxicity, metabolism and resistance. We combine in silico and in vitro experiments at the cellular level, and have access to a robotized high-content imaging lab connected to a modern IT-infrastructure to manage and analyze large-scale data. We are involved in several national and international consortia and have a tight connection to the pharmaceutical industry, Uppsala University Hospital, and Science for Life Laboratory. See the Projects page for more information on our ongoing research projects.
When referring to how workplace related awareness can make a potential difference in worker behavior, a recent study of that phenomena gained national interest. The study examined the opioid drug crisis occurring in the United States. There are many thousands of deaths each year due to opioid overdoses and an estimated nearly 2 million Americans that are addicted to opioids. According to the study, part of the reason that opioid use has vastly increased over the last two decades is as a result of prescribing opioids for pain relief and for similar purposes. Apparently, medical doctors had gotten used to prescribing opioids and did so without necessarily overtly considering the downsides of becoming possibly addicted to it.
While this will yield increased profits for companies who can effectively leverage these technologies into new business models, what makes these developments truly revolutionary is their ability to tackle some of the world's most pressing challenges, ranging from education to health. Experts and fellows from the World Economic Forum's Centre for the Fourth Industrial Revolution weigh in with their predictions for the most exciting ways in which new technologies will improve the state of the world in the coming year. When I was born in 1992, I arrived four months premature with every joint in my body bent together as tightly as possible -- from my head being pressed down on my right shoulder all the way down to my toes being pressed against the bottom of my feet and my ankles collapsed against the back of shins like a broken golf club. My twin sister had shared the same environment with me and was 100% healthy. There was only one culprit: a genetic mutation.