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reading notes of《Artificial Intelligence in Drug Design》
In addition to FAIR principles, Schneider et al. provide an excellent discussion on how data should also follow the ALCOA (Attributable, Legible, Contemporaneous, Original and Accurate) guidelines as defined by US FDA.
As a general principal when an opportunity or challenge is recognized within the drug discovery pipeline, we first ask ourselves if applying machine learning would be a good idea. Are there other methods that may be better as well as quicker to get us the desired information? This leads to investigating the actual use case as well as evaluating the amount and quality of data available for such application.
Generative chemistry methods can combine scoring based on multiparameters to allow picking compounds that check most of the criteria as set by the project teams.
There has been work done to bring chemistry and biology close to each other by utilizing gene expression information in de-novo compound generation.
Potentially possible, it would be useful to allow retrosynthesis be part of the latent space during the generative chemistry process so that users can get synthetically viable compounds.
Various academic groups and industry have invested a lot of resources to provide these models due to the fact that there are frequent late stage failures due to either undesirable ADME properties or toxicity issues. Some of these properties could be measured in a high throughput fashion and thereby leading to generation of large data sets suitable for machine learning.
It’s imperative to discuss a few best practices:
An interesting idea to work on would be to build machine learning models that can utilize predicted ADMET properties in addition to physchem properties and generate low dose compounds.
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