AI revolutionises ageing research: Leads to discovery of promising anti-ageing compounds

A groundbreaking study led by Integrated Biosciences, a biotechnology company focused on ageing research, has showcased the remarkable potential of artificial intelligence (AI) in discovering novel senolytic compounds.

Senolytics are small molecules that can suppress age-related processes such as inflammation, fibrosis, and cancer. By leveraging AI technology to screen over 800,000 compounds, researchers identified three highly efficient drug candidates that exhibit superior medicinal chemistry properties compared to existing senolytics, according to Earth.com.

Unlocking the power of AI in drug discovery

The utilisation of AI in this study marks a significant milestone for longevity research and drug discovery.

By harnessing AI's capabilities to explore chemical space virtually, the researchers at Integrated Bio-sciences have identified multiple promising anti-ageing compounds.

Co-lead author Felix Wong, the co-founder of Integrated Bio-sciences, expressed the significance of these findings and the potential for successful clinical applications.

Targeting senescent cells for age-related diseases

Senescent cells play a role in various age-related diseases, including diabetes, cancer, Alzheimer's disease, and cardiovascular disease.

Senolytics selectively induce apoptosis (programmed cell death) in these non-dividing senescent cells. However, previous senolytic compounds faced challenges such as poor bio-availability and undesirable side effects.

The researchers aimed to identify therapeutic interventions that selectively remove 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 exhibit favourable medicinal chemistry properties, increasing their potential for successful clinical outcomes.

Co-lead author Satotaka Omori, Head of the Ageing Biology department at Integrated Biosciences, expressed optimism about the prospects of these compounds in clinical trials and their potential to restore health in ageing 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, which bind to Bcl-2 (a protein regulating apoptosis and a chemotherapy target), also exhibited favourable toxicity profiles in hemolysis and genotoxicity experiments.

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