3 ms·
They already do. QSAR has been a thing for a long time. The limiting factor is almost always the data. The publicly available SAR data in chembl / pubchem is
by pfisherman 3y ago
They already do. QSAR has been a thing for a long time. The limiting factor is almost always the data. The publicly available SAR data in chembl / pubchem is trash, and why would you spend time and money designing and generating a good training dataset for a model when you could just be optimizing the compound?
Keep in mind that the dataset generated during optimization and the one you would prefer to train a model are quite different.
Also, in practice optimizing a lead compound is about more than just potency. You are also worried about things like toxicity, bioavailability, off target effects (selectivity), synthesis routes / yields (manufacturability), etc. And functional readouts (the assays you run) are not consistent across diseases / indications / targets / compounds.
That being said I am almost certain someone has used Bayesian optimization for an assist.
- dekhn 3y agoIMHO comparing QSAR to modern deep learning methods is sort of like comparing linear regression to modern deep learning methods: a technique that is wholly unsuitable for the sort of problems that deep learning is good at. While I generally agree the data quality is poor, let's be clear: by exploting the data in PDB and Uniprot, Deepmind "solved protein structure prediction". There is a lot more value in the data that's out there, just mostly underutilized.