Ionizable lipids are essential in developing lipid nanoparticles (LNPs) for effective messenger RNA (mRNA) delivery. While traditional methods for designing new ionizable lipids are typically time-consuming, deep generative models have emerged as a powerful solution, significantly accelerating the molecular discovery process. However, a practical challenge arises as the molecular structures generated can often be difficult or infeasible to synthesize. This project explores Monte Carlo tree search (MCTS)-based generative models for synthesizable ionizable lipids. Leveraging a synthetically accessible lipid building block dataset and two specialized predictors to guide the search through chemical space, we introduce a policy network guided MCTS generative model capable of producing new ionizable lipids with available synthesis pathways.

Lipid nanoparticles (LNPs) are vital in modern biomedicine, enabling the effective delivery of mRNA for vaccines and therapies by protecting it from rapid degradation. Among the components of LNPs, ionizable lipids play a key role in RNA protection and facilitate its delivery into the cytoplasm. However, designing ionizable lipids is complex. Deep generative models can accelerate this process and explore a larger candidate space compared to traditional methods. Due to the structural differences between lipids and small molecules, existing generative models used for small molecule generation are unsuitable for lipid generation. To address this, we developed a deep generative model specifically tailored for the discovery of ionizable lipids. Our model generates novel ionizable lipid structures and provides synthesis paths using synthetically accessible building blocks, addressing synthesizability. This advancement holds promise for streamlining the development of lipid-based delivery systems, potentially accelerating the deployment of new therapeutic agents, including mRNA vaccines and gene therapies.

In this study, we generate and maintain a database of 10 million virtual lipids through METiS's in-house de novo lipid generation algorithms and lipid virtual screening techniques. These virtual lipids serve as a corpus for pre-training, lipid representation learning, and downstream task knowledge transfer, culminating in state-of-the-art LNP property prediction performance. We propose LipidBERT, a BERT-like model pre-trained with the Masked Language Model (MLM) and various secondary tasks. Additionally, we compare the performance of embeddings generated by LipidBERT and PhatGPT, our GPT-like lipid generation model, on downstream tasks. The proposed bilingual LipidBERT model operates in two languages: the language of ionizable lipid pre-training, using in-house dry-lab lipid structures, and the language of LNP fine-tuning, utilizing in-house LNP wet-lab data. This dual capability positions LipidBERT as a key AI-based filter for future screening tasks, including new versions of METiS de novo lipid libraries and, more importantly, candidates for in vivo testing for orgran-targeting LNPs. To the best of our knowledge, this is the first successful demonstration of the capability of a pre-trained language model on virtual lipids and its effectiveness in downstream tasks using web-lab data. This work showcases the clever utilization of METiS's in-house de novo lipid library as well as the power of dry-wet lab integration.

Lipidomics generates large data that makes manual annotation and interpretation challenging. Lipid chemical and structural diversity with structural isomers further complicates annotation. Although, several commercial and open-source software for targeted lipid identification exists, it lacks automated method generation workflows and integration with statistical and bioinformatics tools. We have developed the Comprehensive Lipidomic Automated Workflow (CLAW) platform with integrated workflow for parsing, detailed statistical analysis and lipid annotations based on custom multiple reaction monitoring (MRM) precursor and product ion pair transitions. CLAW contains several modules including identification of carbon-carbon double bond position(s) in unsaturated lipids when combined with ozone electrospray ionization (OzESI)-MRM methodology. To demonstrate the utility of the automated workflow in CLAW, large-scale lipidomics data was collected with traditional and OzESI-MRM profiling on biological and non-biological samples. Specifically, a total of 1497 transitions organized into 10 MRM-based mass spectrometry methods were used to profile lipid droplets isolated from different brain regions of 18-24 month-old Alzheimer's disease mice and age-matched wild-type controls. Additionally, triacyclglycerols (TGs) profiles with carbon-carbon double bond specificity were generated from canola oil samples using OzESI-MRM profiling. We also developed an integrated language user interface with large language models using artificially intelligent (AI) agents that permits users to interact with the CLAW platform using a chatbot terminal to perform statistical and bioinformatic analyses. We envision CLAW pipeline to be used in high-throughput lipid structural identification tasks aiding users to generate automated lipidomics workflows ranging from data acquisition to AI agent-based bioinformatic analysis.

Google recently published a scientific paper showing how an artificial intelligence model is able to predict a number of systemic biomarkers from a simple photo of the eye. How were such results arrived at? We discuss this in this article. Diagnosis of disease often requires examinations with expensive instruments and then interpretation by a medical professional who is trained. This is not always possible.

Binah.ai, the number one provider of AI-powered software that enables video-based measurement of physiological health and wellness parameters, announced its revolutionary ability to measure blood count (hemoglobin levels), blood chemistry (hemoglobin A1C) and lipids (cholesterol total) results by simply having users look at a smartphone camera. "Without a doubt, this is a groundbreaking milestone for Binah.ai, and might be the first step towards bloodless blood tests. Our technology demonstrates promising results in measuring this crucial health data, which, alongside the rest of the vital signs we already deliver, is expected to create a shift in health and wellness monitoring," said David Maman, Co-Founder and CEO of Binah.ai. "Leaping from painful and infrequent blood tests, which billions of people can hardly access, to having them available anytime one needs using just a smartphone camera is pretty revolutionary! Video-based blood tests could have a game-changing impact on the healthcare, insurance and wellness industries. Together, we can help bridge the gaps in healthcare and wellness and ensure that no one gets left behind due to a lack of access," added Maman.

Nowadays doctors define and diagnose most diseases on the basis of symptoms. However, that does not necessarily mean that the illnesses of patients with similar symptoms will have identical causes or demonstrate the same molecular changes. In biomedicine, one often speaks of the molecular mechanisms of a disease. This refers to changes in the regulation of genes, proteins or metabolic pathways at the onset of illness. The goal of stratified medicine is to classify patients into various subtypes at the molecular level in order to provide more targeted treatments, wrties the Technical University of Munich in a press release.

A panel of judges at the International Conference for High Performance Computing, Networking, Storage and Analysis (SC19) on Thursday awarded a multi-institutional team led by Lawrence Livermore National Laboratory computer scientists with the conference's Best Paper award. The paper, entitled "Massively Parallel Infrastructure for Adaptive Multiscale Simulations: Modeling RAS Initiation Pathway for Cancer," describes the workflow driving a first-of-its-kind multiscale simulation on predictively modeling the dynamics of RAS proteins -- a family of proteins whose mutations are linked to more than 30 percent of all human cancers -- and their interactions with lipids, the organic compounds that help make up cell membranes. Developed as part of the Pilot 2 project in the Joint Design of Advanced Computing for Cancer program, a collaboration between the Department of Energy (DOE) and National Cancer Institute (NCI), the research resulted in a Multiscale Machine-Learned Modeling Infrastructure (MuMMI) that investigators found was scalable to next-generation heterogenous supercomputers such as LLNL's Sierra and Oak Ridge's Summit. Working for more than two years on the pilot project, which is funded by the National Nuclear Security Administration's Advanced Simulation and Computing program, the multidisciplinary team, composed of more than 20 computational scientists, biophysicists, chemists and statisticians from LLNL, Los Alamos National Laboratory, NCI/Frederick National Laboratory for Cancer Research, Oak Ridge National Laboratory (ORNL) and IBM, ran nearly 120,000 simulations on Sierra, using 5.6 million GPU hours of compute time and generating a massive 320 terabytes of data. "I can't begin to describe how happy I am for our team -- it's been a lot of hard work, and to have it recognized at this level is just amazing," said Francesco Di Natale, LLNL computer scientist and the paper's lead author.

Accurate simulation of cancer-implicated proteins holds enormous promise for basic biomedical science and development of effective therapies, but the high computational cost required has long slowed progress. Recently a multi-institution research team developed a machine learning-based simulation for next-generation supercomputers capable of modeling protein interactions and mutations that play a role in many forms of cancer. Their work on simulating the RAS protein family will be published at SC19 and is a finalist for the Best Paper award. RAS proteins are implicated in roughly one third of cancers, and research to obtain a more detailed understanding of how they interact with the cell's lipid membranes and influence signaling pathways has long been pursued. One way to shortcut the simulations needed and to reduce the computational cost is to use ML to zoom in on areas of interest.

This paper proposes a novel telemedicine system for type 2 diabetes patients. The proposed system supports the patient self-management via a set of telemedicine devices, consisting of health sensors and a smart phone. The proposed system covers not only the sensor data but also the diet (food) and exercise data. To capture the food information, we also developed the voice recognition module focusing on the food names. The basic feasibility of the system is practically demonstrated in the preliminary experiment.