REINVENT 2.0: An AI Tool for De Novo Drug Design

TLDR

Recent advances in deep learning and AI have sparked a renaissance in molecular de novo drug design. The authors present REINVENT, a production‑ready tool for de novo drug design. REINVENT employs graph‑ or SMILES‑based architectures to generate chemical compounds, enabling exploration or exploitation of chemical space in drug discovery projects. REINVENT accelerates idea generation by highlighting promising compounds, and its code is publicly available on GitHub.

Abstract

In the past few years, we have witnessed a renaissance of the field of molecular de novo drug design. The advancements in deep learning and artificial intelligence (AI) have triggered an avalanche of ideas on how to translate such techniques to a variety of domains including the field of drug design. A range of architectures have been devised to find the optimal way of generating chemical compounds by using either graph- or string (SMILES)-based representations. With this application note, we aim to offer the community a production-ready tool for de novo design, called REINVENT. It can be effectively applied on drug discovery projects that are striving to resolve either exploration or exploitation problems while navigating the chemical space. It can facilitate the idea generation process by bringing to the researcher's attention the most promising compounds. REINVENT's code is publicly available at https://github.com/MolecularAI/Reinvent.