Chapter 3. Machine Learning with DeepChem
This chapter provides a brief introduction to machine learning with DeepChem, a library built on top of libraries like TensorFlow and PyTorch to facilitate the use of deep learning in the life sciences. DeepChem provides a large collection of models, algorithms, and datasets that are suited to applications in the life sciences. In the remainder of this book, we will use DeepChem to perform our case studies.
Why Not Just Use Keras, TensorFlow, or PyTorch?
This is a common question. The short answer is that the developers of these packages focus their attention on supporting certain types of use cases that prove useful to their core users. For example, there’s extensive support for image processing, text handling, and speech analysis. But there’s often not a similar level of support in these libraries for molecule handling, genetic datasets, or microscopy datasets. The goal of DeepChem is to give these applications first-class support in the library. This means adding custom deep learning primitives, support for needed file types, and extensive tutorials and documentation for these use cases.
DeepChem is also designed to be well integrated with the TensorFlow and PyTorch ecosystems, so you should be able to mix and match DeepChem code with your other application code based on these frameworks.
In the rest of this chapter, we will assume that you have DeepChem installed on your machine and that you are ready to run the examples. If you ...