Publications by authors named "P O Fedichev"
Article Synopsis
- Recent advancements in aging research and drug discovery connect basic research with clinical applications, aiming to promote healthy longevity in humans.* -
- The Aging Research and Drug Discovery Meeting in 2023 highlighted key areas such as AI, biomarkers, geroscience, and clinical trials focused on enhancing healthspan.* -
- The meeting emphasized the importance of combining generative AI with innovative biological technologies to tackle age-related diseases and extend healthy lifespans.*
Article Synopsis
- Biomarkers of aging (BOA) are special measurements that can help scientists understand how old someone is on a biological level and how this changes with treatments.
- Recently, many new BOA have been discovered that could really help people live healthier lives as they age, but there are some problems getting these ideas into actual medical practice.
- Experts found six main challenges that are stopping BOA from being used more widely and suggested ways to make them better, such as ensuring they are easy to access and useful for everyone.
Article Synopsis
- Research suggests the best lifespan-extending compounds target multiple receptors related to biogenic amines.
- Using graph neural networks, researchers predicted compounds that could potentially extend lifespan and tested them.
- Over 70% of the tested compounds successfully extended lifespan, marking them as highly effective compared to existing data in the DrugAge database.
Article Synopsis
- The search for biomarkers that measure biological aging, especially 'omic'-based ones, has gained momentum, aiming to predict aging outcomes and assess interventions for healthy aging.
- There is currently no agreement on the best methods to validate these aging biomarkers before using them in clinical settings.
- The review emphasizes the need for systematic validation, which can enhance the reliability and effectiveness of aging biomarkers in clinical research and trials focused on longevity.
Deep generative chemistry models emerge as powerful tools to expedite drug discovery. However, the immense size and complexity of the structural space of all possible drug-like molecules pose significant obstacles, which could be overcome with hybrid architectures combining quantum computers with deep classical networks. As the first step toward this goal, we built a compact discrete variational autoencoder (DVAE) with a Restricted Boltzmann Machine (RBM) of reduced size in its latent layer.
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