In the previous articles, we described the problem of Brain Blood Barrier (BBB) permeability and the importance of considering this feature in the drug development process. We also presented the need for constructing ML models that predict substrates and inhibitors of two known BBB influx transporters, OATP1A2 and OATP2B1 . In this article, we describe how we constructed six models that classify drugs into : substrates/non-substrates of BBB transporters inhibitors/non-inhib

In our first article , we have seen that blood-brain barrier (BBB) has a limited permeability for most chemicals and that getting a drug inside the brain, a step key to drug development process, might be tricky. Some drugs can pass through this barrier passively by diffusion and others can only be delivered by specialized proteins named influx transporters. In this second article we are going to get our data ready for Machine Learning models which will predict substrates of

Drug development is a complicated, costly and time-consuming process , where data science and machine learning can bring decisive advantages . For pharmaceutical business, it takes more than ten years and often more than a billion euros to proceed from the first step, target identification, via drug discovery, development and testing, to the final step: drug approval. During this process about 89 % of the candidate molecules fail to become a drug (Kubiniy 2003). Among the

When it comes to healthy food, many may associate it with boiling, steaming, or baking food, without salt, fat, and - alas - without...