3 ms·

that is a great point. for those not familiar with where the money really goes in R&D, and where the biggest opportunities for improvement are, check out these

that is a great point. for those not familiar with where the money really goes in R&D, and where the biggest opportunities for improvement are, check out these charts [1] and [2] from the seminal paper on calculating the cost of getting a drug approved [3]. the biggest areas to improve R&D productivity lie in 1) picking more validated targets to reduce Phase 2 and 3 failure rate and 2) reducing the cost of lead optimization (basically the process of turning a compound that has the desired impact on the target (target is a molecule implicated in disease that you want to effect with a drug) into a molecule with drug like properties (ie gets where you need it in the body, is safe, can be manufactured and delivered efficiently)) im not an AI person, but could AI play a role here? there are plenty of validated targets that are "undruggable"; could AI help find as-yet-undiscovered molecules that could engage these targets? or could AI somehow make the med chem / lead optimization process easier? [1] https://media.nature.com/m685/nature-assets/nrd/journal/v9/n3/images/nrd3078-f2.jpg https://media.nature.com/m685/nature-assets/nrd/journal/v9/n... [2] https://media.nature.com/m685/nature-assets/nrd/journal/v9/n3/images/nrd3078-f3.jpg https://media.nature.com/m685/nature-assets/nrd/journal/v9/n... [3] https://www.nature.com/articles/nrd3078 https://www.nature.com/articles/nrd3078

randcraw 9y ago
The primary missions in drug development are efficacy and safety. AI can help answer clear well-formed efficacy questions like, "Does this molecule fit a chosen target molecule"? But it can't help with bigger efficacies like, "Will hitting this target make a sufficient difference in managing this disease"? Or any safety questions like, "Does the molecule also hit any other molecules somewhere in the body (or population) that might screw up something else"? And unfortunately it's safety failures that eat up 90% of drug development costs (esp. in Phase III). Until Wall St (or Sand Hill Rd) understands that domain-agnostic low-info approaches like AI are incapable of answering complex questions that require teams of PhDs steeped in decades of doing both chemistry and biology, the notion of CADD will continue to miss the mark and waste megabucks.

randcraw 9y ago

The primary missions in drug development are efficacy and safety. AI can help answer clear well-formed efficacy questions like, "Does this molecule fit a chosen target molecule"? But it can't help with bigger efficacies like, "Will hitting this target make a sufficient difference in managing this disease"? Or any safety questions like, "Does the molecule also hit any other molecules somewhere in the body (or population) that might screw up something else"? And unfortunately it's safety failures that eat up 90% of drug development costs (esp. in Phase III). Until Wall St (or Sand Hill Rd) understands that domain-agnostic low-info approaches like AI are incapable of answering complex questions that require teams of PhDs steeped in decades of doing both chemistry and biology, the notion of CADD will continue to miss the mark and waste megabucks.