Deepchem

#Introduction

During the early stages of drug discovery, virtual screening based on artificial intelligence/machine learning/deep learning has become an essential tool.

In this tool, a trained model is used to examine (test)

• a catalogue of small molecules to identify potential drug candidates against a target protein (for drug discovery) or proteins of an organism against a drug (for drug repurposing).

The goal is to either predict

• whether the small molecule interacts with the protein or not (called drug-target interaction prediction, which requires a classifier model with a binary output) or

• an affinity value between the small molecule and the protein (called drug-target affinity prediction, which requires a regression model with a continuous-value output).

Machine learning-based virtual screening methods can be categorized into two types according to the input:

• ligand-based (only the compound/ligand is given as input) and
• pairwise input (both the compound/ligand and the protein are presented as input).

#DepChem

https://www.openchemistry.org/gsoc/

DeepChemm

uv install deepchem

#Linux

Install Uv:

curl -LsSf https://astral.sh/uv/install.sh | sh
source $HOME/.local/bin/env

We are going to use a model based on tensorflow, because of that we’ve added [tensorflow] to the uv add command to ensure the necessary dependencies are also installed

uv init chem
cd chem
uv add deepchem tensorflow[and-cuda]

Install PyCharm

tar xzf pycharm-*.tar.gz -C
cd pycharm-*/bin
sh pycharm.sh