A groundbreaking study conducted by Integrated Biosciences, a biotechnology company focused on aging research, has highlighted the significant potential of artificial intelligence (AI) in the discovery of new senolytic compounds.
Senolytics are small molecules that can suppress age-related processes such as inflammation, fibrosis, and cancer. By utilizing AI technology to screen over 800,000 compounds, researchers have identified three highly effective drug candidates that possess superior medicinal chemistry properties compared to existing senolytics, as reported by Earth.com.
The use of AI in this study represents a major milestone in longevity research and drug discovery. Through harnessing the capabilities of AI to explore chemical space virtually, the scientists at Integrated Biosciences have identified several promising compounds with anti-aging potential.
Co-lead author Felix Wong, the co-founder of Integrated Biosciences, emphasized the significance of these findings and their potential for successful clinical applications.
Senescent cells play a role in various age-related diseases, including diabetes, cancer, Alzheimer’s disease, and cardiovascular disease. Senolytics selectively induce programmed cell death in these non-dividing senescent cells. However, previous senolytic compounds have faced challenges such as poor bioavailability and undesirable side effects.
The researchers aimed to identify therapeutic interventions that selectively eliminate senescent cells from the body without harming healthy cells. Through their AI-driven approach, they discovered three highly selective and potent senolytic compounds.
These compounds possess favorable medicinal chemistry properties, increasing their potential for successful clinical outcomes. Co-lead author Satotaka Omori, Head of the Aging Biology department at Integrated Biosciences, expressed optimism about the prospects of these compounds in clinical trials and their potential to restore health in aging individuals.
To predict the senolytic activity of a wide range of molecules, the scientists trained deep neural networks using experimentally generated data. Leveraging this machine learning model, they screened over 800,000 compounds and identified three compounds that demonstrated high selectivity and potency as senolytics.
These compounds bind to Bcl-2, a protein regulating apoptosis and a target for chemotherapy, and also displayed favorable toxicity profiles in hemolysis and genotoxicity experiments.
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