Existing protein-ligand docking studies typically focus on the self-docking scenario, which is less practical in real applications. Moreover, some studies involve heavy frameworks requiring extensive training, posing challenges for convenient and efficient assessment of docking methods. To fill these gaps, we design PoseX, an open-source benchmark to evaluate both self-docking and cross-docking, enabling a practical and comprehensive assessment of algorithmic advances. Specifically, we curated a novel dataset comprising 718 entries for self-docking and 1,312 entries for cross-docking; second, we incorporated 23 docking methods in three methodological categories, including physics-based methods (e.g., Schrödinger Glide), AI docking methods (e.g., DiffDock) and AI co-folding methods (e.g., AlphaFold3); third, we developed a relaxation method for post-processing to minimize conformational energy and refine binding poses; fourth, we built a leaderboard to rank submitted models in real-time. We derived some key insights and conclusions from extensive experiments: (1) AI approaches have consistently outperformed physics-based methods in overall docking success rate. (2) Most intra- and intermolecular clashes of AI approaches can be greatly alleviated with relaxation, which means combining AI modeling with physics-based post-processing could achieve excellent performance. (3) AI co-folding methods exhibit ligand chirality issues, except for Boltz-1x, which introduced physics-inspired potentials to fix hallucinations, suggesting modeling on stereochemistry improves the structural plausibility markedly. (4) Specifying binding pockets significantly promotes docking performance, indicating that pocket information can be leveraged adequately, particularly for AI co-folding methods, in future modeling efforts. The code, dataset, and leaderboard are released at https://github.com/CataAI/PoseX.

Artificial intelligence (AI) has the potential to revolutionize the drug discovery process, offering improved efficiency, accuracy, and speed. However, the successful application of AI is dependent on the availability of high-quality data, the addressing of ethical concerns, and the recognition of the limitations of AI-based approaches. In this article, the benefits, challenges and drawbacks of AI in this field are reviewed, and possible strategies and approaches for overcoming the present obstacles are proposed. The use of data augmentation, explainable AI, and the integration of AI with traditional experimental methods, as well as the potential advantages of AI in pharmaceutical research are also discussed. Overall, this review highlights the potential of AI in drug discovery and provides insights into the challenges and opportunities for realizing its potential in this field. Note from the human-authors: This article was created to test the ability of ChatGPT, a chatbot based on the GPT-3.5 language model, to assist human authors in writing review articles. The text generated by the AI following our instructions (see Supporting Information) was used as a starting point, and its ability to automatically generate content was evaluated. After conducting a thorough review, human authors practically rewrote the manuscript, striving to maintain a balance between the original proposal and scientific criteria. The advantages and limitations of using AI for this purpose are discussed in the last section.

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A new artificial-intelligence-based approach can predict if and when a patient could die of cardiac arrest, a recent study led by researchers at John Hopkins University has found. The technology, built on raw images of patients' diseased hearts and patient backgrounds, stands to revolutionize clinical decision-making and increase survival from sudden and lethal cardiac arrhythmias, one of medicine's deadliest and most puzzling conditions. The new study was published in the journal, 'Nature Cardiovascular Research'. "Sudden cardiac death caused by arrhythmia accounts for as many as 20 per cent of all deaths worldwide and we know little about why it's happening or how to tell who's at risk," said senior author Natalia Trayanova, the Murray B. Sachs Professor of Biomedical Engineering and Medicine. "There are patients who may be at low risk of sudden cardiac death getting defibrillators that they might not need and then there are high-risk patients that aren't getting the treatment they need and could die in the prime of their life. What our algorithm can do is determine who is at risk for cardiac death and when it will occur, allowing doctors to decide exactly what needs to be done," she added.

Anti-Hindu sentiment has been on the rise in recent years. It is a sentiment that advances, amplifies, and articulates hatred against Hindus and Hinduism. This is a toxic global phenomenon influenced by legacies of the past. Hinduism and Hindus have been at the receiving end of such hate since colonial times. This hate sentiment is now known as Hinduphobia, Hindumisia, or Hindudvesha .

Argonne National Laboratory researchers uncovered and continue to explore new ways to advance a semiconductor chips design technique using artificial intelligence. They present several AI-based approaches to optimize atomic layer deposition, or ALD, processes in a recently published study. The method produces super-fine films of materials, like one atom thick. It also partly underpins the making of computer chips, which are now at the center of a global supply chain shortage that's pushed up prices of all sorts of electronics. "The effort predates the current chip shortage issues, but we have been looking at semiconductor processing and its manufacturing challenges for a long time," ARNL Principal Materials Scientist Angel Yanguas-Gil told Nextgov Thursday.

"What we are now able to demonstrate offers fantastic potential for a number of future applications, such as faster and more cost-efficient development of protein-based drugs," says Aleksej Zelezniak, Associate Professor at the Department of Biology and Biological Engineering at Chalmers. Proteins are large, complex molecules that play a crucial role in all living cells, building, modifying, and breaking down other molecules naturally inside our cells. They are also widely used in industrial processes and products, and in our daily lives. Protein-based drugs are very common--the diabetes drug insulin is one of the most prescribed. Some of the most expensive and effective cancer medicines are also protein-based, as well as the antibody formulas currently being used to treat COVID-19.

With a deepening focus on unleashing novel applications of artificial intelligence (AI) across--and beyond--neurosurgery, a multidisciplinary team of physicians and mathematicians are collaborating on advanced approaches to diagnosis and patient care, developing data-driven methods that hold potential for progress across the continuum of medicine. Investigations into clinical applications for AI, with a focus on neurosurgical care, have gained significant momentum with the recruitment of Eric K. Oermann, MD, assistant professor in the Departments of Neurosurgery and Radiology and a leading expert in AI applications in medicine. Dr. Oermann brings deep expertise at the intersection of neurosurgery and mathematics to research projects that apply data science and algorithms to answer pressing neurosurgical questions as well as those that apply to medicine far beyond neurosurgery. "Neurosurgery tends to be the technical spearhead of the broader medical world, innovating to benefit our own patients and medicine with a capital M," he says. "So our discoveries in AI are at the next forefront of technological innovation in medicine, writ large." Dr. Oermann developed the vision for his research in close partnership with Daniel A. Orringer, MD, associate professor in the Departments of Neurosurgery and Pathology.

IT service operations teams and their leaders are in the middle of the busiest weeks of their careers right now. They're scrambling to help many first-time work-from-home employees get securely connected as ITSM systems bog down under the weight of workloads they weren't designed for. Incident queues are thousands of requests long in many companies, waiting for assignment. A quick Pareto Analysis of an Incident Management queue with trouble tickets shows that approximately 75% to 80% of the requests for service are from the top 20% of connectivity and security login issues all IT users face. Adopting an AI-based approach to Incident Deflection that seeks the best IT service resource starting with help files and videos and then progressing to an IT service agent would reduce the queue quickly.

The technology of AI has been improving every year for the past 20 years, and today it is a very mature technology. Many companies and organizations are actively employing AI in different ways. AI is also developing into the next generation of computing, where big ideas can come from and many people can become an expert on a new discipline. Today, there are many companies working on various AI projects that are shaping the future of the technology. While AI is generally synonymous with artificial intelligence, more technically sophisticated systems can be described as "artificial general intelligence".