4 Solubility Deep Dive with Linear Models
This chapter covers
- Solubility and how to model it with linear regression.
- The mechanics of how linear models are trained.
- A tour of linear models accessible via Scikit-Learn.
- How to evaluate a model’s regression performance and applicability domain.
- What causes overfitting and how we mitigate it by analyzing a model’s bias-variance trade-offs.
In chapter 2, we reviewed common compound filters, such as Lipinski’s Rule of Five, that define criteria that act as a proxy for a compound’s drug-likeness. A critical factor in the development of pharmaceutical compounds is drug solubility. The ability of a drug candidate to dissolve in biological fluids, such as water or gastric juices, directly impacts its bioavailability, efficacy, and ultimately its success as a therapeutic agent. Poor solubility can lead to reduced absorption, necessitating higher drug doses and potentially causing adverse effects, while good solubility can enhance a drug's therapeutic profile.