This repository contains the lab notebooks used in the "Software Engineering for Molecular Sciences" course taught at UC Berkeley. The code and notebooks are released under the Apache 2.0 license. Several of the lab notebooks are inspired by material from "Machine Learning for Drug Discovery" by Noah Flynn (Manning Publications,https://www.manning.com/books/machine-learning-for-drug-discovery).
To run and modify the code locally, install Anaconda (or Miniconda) and git if you don't already have access to them. Clone this repository by typing the following within a terminal (ignoring the first $ character):
$ git clone https://github.com/nrflynn2/swe-molecular-sciences.git
$ cd swe-molecular-sciences
Set up a conda environment with prerequisite installs:
$ conda create --name swe-molecular-sciences python=3.10 pip
$ conda activate swe-molecular-sciences
$ pip install -r requirements.txt
Finally, start Jupyter in the terminal via jupyter notebook or through your favorite IDE (e.g., Visual Studio Code) to embark on an exciting journey. Happy learning!