⚠ Archived — the upstream repository is no longer receiving updates.
REINVENT
Industrial-grade reinforcement-learning-based generative platform for de novo molecular design with transformer architectures, supporting multi-objective optimization, scaffold decoration, and curriculum learning (AstraZeneca MolecularAI, REINVENT 4, 2024)
README
IMPORTANT: This repository is in archive mode meaning that it is read only and will not undergo further changes. All further development will be done on REINVENT 4. REINVENT 3.2 ================================================================================================================= Installation Install Conda Clone this Git repository Open a shell, and go to the repository and create the Conda environment: $ conda env create -f reinvent.yml Activate the environment: $ conda activate…
- Repository
- github.com/molecularai/reinvent
Source attribution
- GitHub — github.com/molecularai/reinvent
- Awesome AI for Science — github.com/molecularai/reinvent
Related resources
Deep learning library for Chemistry based on Tensorflow
A library for building, manipulating, analyzing and automatic design of molecules, including a genetic algorithm.
Diffusion model for scalable protein structure design with multi-motif scaffolding capabilities, achieving state-of-the-art designability, diversity, and novelty through SE(3)-equivariant attention and massive data augmentation (AlQuraishi Lab, 2024)
General multimodal protein design framework enabling DNA-encoding of chemistry for programmable enzyme design and diverse protein generation through diffusion-based generative modeling (190+ stars, Apache 2.0, 2026)
The fmcsR package introduces an efficient maximum common substructure (MCS) algorithms combined with a novel matching strategy that allows for atom and/or bond mismatches in the substructures shared among two small molecules. The resulting flexible MCSs (FMCSs) are often larger than strict MCSs, resulting in the identification of more common features in their source structures, as well as a higher sensitivity in finding compounds with weak structural similarities. The fmcsR package provides several utilities to use the FMCS algorithm for pairwise compound comparisons, structure similarity searching and clustering.