Machine learning has great applications for biology. But beware that some of the media headlines are far too often focused on the intersection with Tech/Deep Learning, which aim mostly at solving a specific problem of biology. But it tells absolutely nothing about disease mechanism and does not answer why some combination of drugs can be synergistic or not in certain disease. It’s almost like engineers trying to find a use case to their fancy algorithm. And Tech VC pouring money into this because they understand the tech language but totally missed the point of patient survival.
Let’s say I use AI to generate an ideal small molecule as anti cancer drug. Nice, but what if the best way to make an impact in cancer is through cell therapy and not small molecule ? To use AI and ML for these applications is much harder from the scientific perspective, as we simply do not have enough quality data. I have some hope in Graph neural network, factor analysis and any basic ML, as long as everything is connected: Patient data (treatment+outcome) + genomics + In vitro/in vivo data
I totally agree. The techniques that have worked well so far are quite far from understanding cells, organs, organisms, or ecosystems. However, the incredible rate of progress at the small molecule to protein complex scale is already showing a strong impact, at least on getting a much better funnel before we begin in vivo testing.
Machine learning has great applications for biology. But beware that some of the media headlines are far too often focused on the intersection with Tech/Deep Learning, which aim mostly at solving a specific problem of biology. But it tells absolutely nothing about disease mechanism and does not answer why some combination of drugs can be synergistic or not in certain disease. It’s almost like engineers trying to find a use case to their fancy algorithm. And Tech VC pouring money into this because they understand the tech language but totally missed the point of patient survival.
Let’s say I use AI to generate an ideal small molecule as anti cancer drug. Nice, but what if the best way to make an impact in cancer is through cell therapy and not small molecule ? To use AI and ML for these applications is much harder from the scientific perspective, as we simply do not have enough quality data. I have some hope in Graph neural network, factor analysis and any basic ML, as long as everything is connected: Patient data (treatment+outcome) + genomics + In vitro/in vivo data
I totally agree. The techniques that have worked well so far are quite far from understanding cells, organs, organisms, or ecosystems. However, the incredible rate of progress at the small molecule to protein complex scale is already showing a strong impact, at least on getting a much better funnel before we begin in vivo testing.