Recently Y Combinator announced their intent to fund companies working on treatments for aging. It is one of the many signs of a growing interest in this area of development in the venture community. One of the early results appears to be more funding for computational methods of improving drug discovery, with therapies for aging as the rallying cry, after the established Insilico Medicine model. It makes sense that a primarily software-focused part of the venture community would move into a new area, biotechnology, by funding ventures that apply computational technology to the space. That says nothing about the effectiveness of the approach, of course, just that it is a natural evolution of established knowledge and interests.
There is certainly a lot of room for improvement when it comes to the cost and effort required to find and prove out small molecule and other drugs to treat specific conditions or target specific biological mechanisms with minimal side-effects. It is reasonable to think that established deep learning approaches can be fruitfully applied here, to focus attention on molecules in the standard libraries that might otherwise be overlooked, and to design new therapeutic molecules based
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