Molecular property prediction is essential for drug discovery. In recent years, deep learning methods have been introduced to this area and achieved state-of-the-art performances. However, most of existing methods ignore the intrinsic relations between molecular properties which can be utilized to improve the performances of corresponding prediction tasks. In this paper, we propose a new approach, namely Physics properties Enhanced Molecular Property prediction (PEMP), to utilize relations between molecular properties revealed by previous physics theory and physical chemistry studies. Specifically, we enhance the training of the chemical and physiological property predictors with related physics property prediction tasks. We design two different methods for PEMP, respectively based on multi-task learning and transfer learning. Both methods include a model-agnostic molecule representation module and a property prediction module. In our implementation, we adopt both the state-of-the-art molecule embedding models under the supervised learning paradigm and the pretraining paradigm as the molecule representation module of PEMP, respectively. Experimental results on public benchmark MoleculeNet show that the proposed methods have the ability to outperform corresponding state-of-the-art models.

… Administration for an advanced spinal cord stimulator (SCS) that uses artificial intelligence to individualize treatment for each patient.

Pulmonary hypertension is a severe condition that occurs when the pressure in the vessels that carry blood from the heart to the lungs is higher than normal, causing undo stress on the heart. PH affects up to 1% of the global population and is a marker of poor health outcomes.¹ PH can cause premature disability, heart failure, and death. Unfortunately, delays of over two years frequently occur between the onset of symptoms and diagnosis of severe kinds of PH. The gold standards for diagnosing PH are echocardiography and right heart catheterization, which are costly, invasive, and require a heart specialist.

Finding new ways to treat the novel coronavirus and its ever-changing variants has been a challenge for researchers, especially when the traditional drug development and discovery process can take years. A Michigan State University researcher and his team are taking a hi-tech approach to determine whether drugs already on the market can pull double duty in treating new COVID variants. "The COVID-19 virus is a challenge because it continues to evolve," said Bin Chen, an associate professor in the College of Human Medicine. "By using artificial intelligence and really large data sets, we can repurpose old drugs for new uses." Chen built an international team of researchers with expertise on topics ranging from biology to computer science to tackle this challenge.

The vast majority of FDA-approved medical devices enabled by artificial intelligence or machine learning are concentrated in radiology and cardiovascular care, according to an analysis by Rock Health. Rock Health used data from FDA clearances and approvals from 1997 to 2021 to determine where these devices are used the most. Here are the AI/machine-learning enabled devices by therapeutic area, the Oct. 8 report found:

Drug recommendation assists doctors in prescribing personalized medications to patients based on their health conditions. Existing drug recommendation solutions adopt the supervised multi-label classification setup and only work with existing drugs with sufficient prescription data from many patients. However, newly approved drugs do not have much historical prescription data and cannot leverage existing drug recommendation methods. To address this, we formulate the new drug recommendation as a few-shot learning problem. Yet, directly applying existing few-shot learning algorithms faces two challenges: (1) complex relations among diseases and drugs and (2) numerous false-negative patients who were eligible but did not yet use the new drugs. To tackle these challenges, we propose EDGE, which can quickly adapt to the recommendation for a new drug with limited prescription data from a few support patients. EDGE maintains a drug-dependent multi-phenotype few-shot learner to bridge the gap between existing and new drugs. Specifically, EDGE leverages the drug ontology to link new drugs to existing drugs with similar treatment effects and learns ontology-based drug representations. Such drug representations are used to customize the metric space of the phenotype-driven patient representations, which are composed of a set of phenotypes capturing complex patient health status. Lastly, EDGE eliminates the false-negative supervision signal using an external drug-disease knowledge base. We evaluate EDGE on two real-world datasets: the public EHR data (MIMIC-IV) and private industrial claims data. Results show that EDGE achieves 7.3% improvement on the ROC-AUC score over the best baseline.

Treatments targeting complex diseases, such as cancer, frequently lead to acquired drug resistance, due to patient-specific variability. For instance, drugs targeting only one key component of growth or proliferation pathways, may lead to selective pressure and activation of a compensatory mechanism [1], thus making this treatment suboptimal. However, during multi-target inhibition with reduced stringency, drug resistance is less likely. Therefore, the implementation of combination therapy might improve patient treatment as different drugs may target distinct pathways or genes, likely leading to decreased cancer cell survival. In addition to the increased efficacy, combination therapy often reduces toxicity and decreases the likelihood of treatment resistance compared to monotherapy (i.e., single drug) treatments [2]. Due to advancements in high-throughput screening (HTS), the number of drug screening datasets has been growing in recent years. Some examples include the NCI-ALMANAC dataset [3] which contains 103 FDA-approved drugs tested in 60 different cell lines (NCI-60) [4] or the large oncology dataset produced by Merck&Co [5] which is composed of 38 drugs tested in 39 different cell lines from 6 different tissue types.

Since 1995, the FDA has authorized more than 500 AI/ML-enabled medical devices via 510(k) clearance, granted De Novo request, or approved PMA. This week the FDA published an updated list with 178 new devices that were authorized through July 2022. According to the FDA, their list is based on publicly available information and is not a comprehensive resource of FDA approved AI/ML-enabled medical devices. In today's DeepTech newsletter I'm sharing a high level analysis of the 521 devices on the list, charts to visualize the data, and a summary of milestones. Note: According to the FDA their list is based on publicly available information and is not a comprehensive resource of approved AI/ML-enabled medical devices.

Dr. Ronny Shalev is CEO and founder of Dyad Medical Inc. a company that develops FDA-cleared software which automatically analyzes the content of cardiac and cardiovascular images using artificial intelligence. He has spent much of the past 25 years in executive positions, including VP of Sales and Marketing at Orbotech (NASDAQ: ORBK), where he managed teams of more than 100 people worldwide and Director of the World-wide Program Management at Marvell Semiconductor (NASDAQ: MRVL). He has a significant amount of experience as an entrepreneur and is dedicated to using his skills to help physicians make accurate decisions to improve patient outcomes. He holds a Ph.D. in electrical engineering and computer science from Case Western Reserve University. What initially attracted you to computer science and machine learning?

AI is frequently implemented as a hardware and software hybrid system. From a software perspective, algorithms are the major focus of AI. Creating AI algorithms can be conceptualized using an Artificial Neural Network. It is a simulation of the human brain made up of a network of neurons connected by weighted communication pathways. Artificial intelligence is used in computers to refer to a computer program's ability to carry out operations linked to human intellect, such as reasoning and learning.