A View of How Machine Learning in Drug Discovery Works in Practice

A fairly broad effort is underway to make small molecule drug discovery faster, cheaper, and less onerous by employing machine learning strategies. Implementations of this and related approaches are common in the growing longevity industry, for reasons that may have to do with the overlap of interests in longevity and artificial intelligence in the Bay Area entrepreneurial and venture communities, where it is comparatively common for people to make the leap from the software industry to biotechnology, and look for ways to apply their existing skills to a new industry.

A number of drug development platform companies have at least started out with a focus on aging, such as Insilico Medicine, BioAge, and so forth. If you are curious about how one goes about accelerating small molecule drug discovery in this way, look no further than this open access paper, which discusses some of Insilico Medicine's recent work in enough detail to get a taste of it.

Aging biology is a promising and burgeoning research area that can yield dual-purpose pathways and protein targets that may impact multiple diseases, while retarding or possibly even reversing age-associated processes. One widely used approach to classify a multiplicity of mechanisms driving the aging process is the hallmarks of aging. In addition to the classic nine hallmarks of aging, processes such as extracellular matrix stiffness, chronic inflammation, and activation of retrotransposons are also often considered, given their strong association with aging.

In this study, we used a variety of target identification and prioritization techniques offered by the AI-powered PandaOmics platform, to propose a list of promising novel aging-associated targets that may be used for drug discovery. We also propose a list of more classical targets that may be used for drug repurposing within each hallmark of aging. Most of the top targets generated by this comprehensive analysis play a role in inflammation and extracellular matrix stiffness, highlighting the relevance of these processes as therapeutic targets in aging and age-related diseases.

Overall, our study reveals both high confidence and novel targets associated with multiple hallmarks of aging and demonstrates application of the PandaOmics platform to target discovery across multiple disease areas.

Link: https://doi.org/10.18632/aging.203960

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