jcathalina / rxitect

A collection of deep reinforcement learning-based & GFlowNet drug molecule generators focused on generation of molecules using Graphs/SELFIES guided by modern retrosynthesis tools to increase synthetic accessibility of de-novo designed drugs.

Rxitect

A de-novo drug design library for creating retrosynthesis-aware reward-driven models

Introduction

This library was made for my M.Sc. thesis research with the aim of understanding how computational chemists can incorporate synthesis planning into de-novo drug design systems. Many molecule generators propose interesting but impractical molecules, which is why we need to design them with synthesizability in mind. Modern Computer-Assisted Synthesis Planning (CASP) tools are quite powerful but are of limited use in algorithms that need to call said tools many times (e.g., > 100.000 calls) due to the time it takes to solve a single molecule on average. This research aims to create a useful proxy that is cheap to call yet robust, and then using a myriad of techniques that are known to be effective in searching the vast molecular search space such as Reinforcement Learning (RL), and Generative Flow Networks (GFlowNets), we can experimentally test if these proxies are useful to propose more practical and synthesizable molecules.

Quickstart

Run the following code to get up and running ` conda env create -f environment.yml # alternatively you can use mamba, which I recommend conda activate rx poetry install`

Examples

Coming Soon!