Abstract: The COVID-19 pandemic has initiated a global health emergency, with an exigent need for effective cure. Progressively, drug repurposing is emerging as a promising solution as it saves the time, cost and labor. However, the number of drug candidates that have been identified as being repurposed for the treatment of COVID-19 are still insufficient, so more effective and thorough drug repurposing strategies are required. In this study, we joint the molecular docking with machine learning regression approaches to find some prospective therapeutic candidates for COVID-19 treatment. We screened the 5903 approved drugs for their inhibition by targeting the main protease 3CL of SARS-CoV-2, which is responsible to replicate the virus. Molecular docking is used to calculate the binding affinities of these drugs to the main protease 3CL. We employed several machine learning regression approaches for QSAR modeling to find out some potential drugs with high binding affinity. We shortlisted six favorable drugs and examined their physiochemical and pharmacokinetic properties of these top-ranked selected drugs and their best binding interaction for specific target protease 3CLpro. Our study provides an efficient framework for drug repurposing against COVID-19, and establishes the potential of combining molecular docking with machine learning regression approaches to accelerate the identification of potential therapeutic candidates. Our findings contribute to the larger goal of finding effective cures for COVID-19, which is an acute global health challenge. Keywords: COVID-19; main protease 3CL; drug repurposing; QSAR model; binding affinity; molecular docking 1 Introduction The COVID-19 outbreak has presented an unprecedented worldwide health emergency, with over 687 million confirmed cases and over 6.8 million deaths globally as of May 2023 according to https://www.worldometers.info/coronavirus/. At present, there is no certain drug available to treat COVID-19, and the development of effective cures has become a priority for researchers globally [1]. COVID-19 is triggered by SARS-CoV-2, a positive-sense single-stranded RNA virus that mainly infects the respiratory tract of humans [2]. When the spike protein attaches to the ACE2 receptor on the surface of human cells, the virus enters the cell, and then it utilizes the host's cellular machinery to replicate and spread throughout the body. Figure 1 depicts the life cycle of a coronavirus.

To provide women at a high risk of breast cancer with more frequent screenings between mammograms, researchers at the Massachusetts Institute of Technology (MIT) are developing a wearable ultrasound scanner designed to be attached to a bra. The goal is to help women detect breast cancer tumors in the early stages and maximize the survival rate, according to a press release on MIT's website. The researchers' aim was to design a wearable "miniaturized ultrasound device" that allows for "consistent placement and orientation" to take images of breast tissue, according to lead study author Canan Dagdeviren, PhD, associate professor at MIT. WHAT IS ARTIFICIAL INTELLIGENCE (AI)? The device attaches to the bra like a patch, with a "honeycomb" pattern that has open spaces for the tracker to move through for an optimal field of view, Dagdeviren told Fox News Digital. "The ultrasound generates a wave that penetrates the targeted breast tissue," he said.

Artificial Intelligence in Medicine has made significant progress with emerging applications in medical imaging, patient care, and other areas. While these applications have proven successful in retrospective studies, very few of them were applied in practice.The field of Medical AI faces various challenges, in terms of building user trust, complying with regulations, using data ethically.Explainable AI (XAI) aims to enable humans understand AI and trust its results. This paper presents a literature review on the recent developments of XAI solutions for medical decision support, based on a representative sample of 198 articles published in recent years. The systematic synthesis of the relevant articles resulted in several findings. (1) model-agnostic XAI techniques were mostly employed in these solutions, (2) deep learning models are utilized more than other types of machine learning models, (3) explainability was applied to promote trust, but very few works reported the physicians participation in the loop, (4) visual and interactive user interface is more useful in understanding the explanation and the recommendation of the system. More research is needed in collaboration between medical and AI experts, that could guide the development of suitable frameworks for the design, implementation, and evaluation of XAI solutions in medicine.

The programme of the International Conference on Machine Learning (ICML) featured an invited talk by Jennifer Doudna entitled "The future of ML in biology: CRISPR for health and climate". Jennifer Doudna and Emmanuelle Charpentier won the 2020 Nobel Prize in Chemistry for "the development of a method for genome editing". The method in question is often referred to as CRISPR/Cas9 genetic scissors. Using this technique, researchers can change the DNA of animals, plants and microorganisms with extremely high precision. This technology has already had a huge impact on the biological sciences.

Fox News correspondent Gillian Turner has the latest on the president's focus amid calls for an impeachment inquiry on'Special Report.' Silicon Valley has started scooping up military contracts as the Pentagon turns to private companies to boost artificial intelligence (AI) development and adoption, according to reports. "This kind of change doesn't always move as smoothly or as quickly as I'd like," Defense Secretary Lloyd Austin said during a speech in December to a group that included start-up tech companies. The courtship between tech start-ups and the Department of Defense (DOD) started well before the public engagement with large language models (LLMs) like ChatGPT: Saildrone, a start-up founded in 2013, had started developing an armada of AI systems to conduct surveillance on international waters in 2021. Alexander Karp, CEO and co-founder of Palantir Technologies, wrote an open letter to European leaders just weeks after Russia invaded Ukraine February 2022 and urged them to modernize their armies with Silicon Valley's help.

Fox News contributor Dr. Marc Siegel weighs in on how artificial intelligence can change the patient-doctor relationship on'America's Newsroom.' Artificial intelligence-powered ultrasounds are now one step closer to becoming part of routine prenatal care. Sonio Detect, an AI-powered ultrasound scanning technology, has become the first product of its kind to land FDA approval. Made by Sonio, a "femtech" company based in Paris, France, the AI product functions as a high-tech helper for maternity care professionals, scanning for warning signs that could indicate fetal health issues. WHAT IS ARTIFICIAL INTELLIGENCE (AI)?

Machine learning (ML) is a promising approach for predicting small molecule properties in drug discovery. Here, we provide a comprehensive overview of various ML methods introduced for this purpose in recent years. We review a wide range of properties, including binding affinities, solubility, and ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity). We discuss existing popular datasets and molecular descriptors and embeddings, such as chemical fingerprints and graph-based neural networks. We highlight also challenges of predicting and optimizing multiple properties during hit-to-lead and lead optimization stages of drug discovery and explore briefly possible multi-objective optimization techniques that can be used to balance diverse properties while optimizing lead candidates. Finally, techniques to provide an understanding of model predictions, especially for critical decision-making in drug discovery are assessed. Overall, this review provides insights into the landscape of ML models for small molecule property predictions in drug discovery. So far, there are multiple diverse approaches, but their performances are often comparable. Neural networks, while more flexible, do not always outperform simpler models. This shows that the availability of high-quality training data remains crucial for training accurate models and there is a need for standardized benchmarks, additional performance metrics, and best practices to enable richer comparisons between the different techniques and models that can shed a better light on the differences between the many techniques.

Multiple sclerosis (MS) is a debilitating neurological disease affecting nearly one million people in the United States. Sphingosine-1-phosphate receptor 1, or S1PR1, is a protein target for MS. Siponimod, a ligand of S1PR1, was approved by the FDA in 2019 for MS treatment, but there is a demonstrated need for better therapies. To this end, we finetuned an autoencoder machine learning model that converts chemical formulas into mathematical vectors and generated over 500 molecular variants based on siponimod, out of which 25 compounds had higher predicted binding affinity to S1PR1. The model was able to generate these ligands in just under one hour. Filtering these compounds led to the discovery of six promising candidates with good drug-like properties and ease of synthesis. Furthermore, by analyzing the binding interactions for these ligands, we uncovered several chemical properties that contribute to high binding affinity to S1PR1. This study demonstrates that machine learning can accelerate the drug discovery process and reveal new insights into protein-drug interactions.

We introduce AbDiffuser, an equivariant and physics-informed diffusion model for the joint generation of antibody 3D structures and sequences. AbDiffuser is built on top of a new representation of protein structure, relies on a novel architecture for aligned proteins, and utilizes strong diffusion priors to improve the denoising process. Our approach improves protein diffusion by taking advantage of domain knowledge and physics-based constraints; handles sequence-length changes; and reduces memory complexity by an order of magnitude enabling backbone and side chain generation. Numerical experiments showcase the ability of AbDiffuser to generate antibodies that closely track the sequence and structural properties of a reference set. Laboratory experiments confirm that all 16 HER2 antibodies discovered were expressed at high levels and that 57.1% of selected designs were tight binders. We focus on the generation of immunoglobulin proteins, also known as antibodies, that help the immune ...

This white paper is a response to the "AI Accountability Policy Request for Comments" by the National Telecommunications and Information Administration of the United States. The question numbers for which comments were requested are provided in superscripts at the end of key sentences answering the respective questions. The white paper offers a set of interconnected recommendations for an AI accountability policy.