Two recent articles on the impact of AI/ML and computation in biology caught my attention, one by Andrew Dunn with the provocative title “In a reality check for the field, AI underwhelms in Leash Bio's binding contest: 'No one did well'“ and the more concisely titled “
Certainly one of the more balanced articles on AI/ML in drug discovery that I've encountered. The authors have skilfully avoided embracing the extreme positions that are the default of either the Tech Bio folks or the skeptics. I remain interested in better understanding how these technologies will better predict P2 outcomes including patient election and indication prioritization.
Awesome writeup! Enabling more efficient decision making is going to have the biggest impact of ML based tools in biotechs. Always surprised by some of the time-intensive manual or sometimes borderline tasks that chemists and scientists have to do for derisking projects or pushing a compound forward
Certainly one of the more balanced articles on AI/ML in drug discovery that I've encountered. The authors have skilfully avoided embracing the extreme positions that are the default of either the Tech Bio folks or the skeptics. I remain interested in better understanding how these technologies will better predict P2 outcomes including patient election and indication prioritization.
Awesome writeup! Enabling more efficient decision making is going to have the biggest impact of ML based tools in biotechs. Always surprised by some of the time-intensive manual or sometimes borderline tasks that chemists and scientists have to do for derisking projects or pushing a compound forward