REINVENT4
REINVENT4 is an AI-powered Python tool for generating and optimizing small molecules in drug discovery. It uses reinforcement learning and transfer learning to design molecules meeting specified chemical properties, supporting tasks like scaffold hopping and de novo design.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | MolecularAI/REINVENT4 |
| Owner | MolecularAI |
| Primary language | Python |
| License | Apache-2.0 — OSI-approved |
| Stars | 811 |
| Forks | 221 |
| Open issues | 3 |
| Latest release | v4.8 (2026-06-16) |
| Last updated | 2026-06-22 |
| Source | https://github.com/MolecularAI/REINVENT4 |
What REINVENT4 is
REINVENT4 implements RL-driven molecular generation with multi-component scoring, transfer learning for prior training, and GPU acceleration (NVIDIA/AMD/Intel/Apple). It requires Python ≥3.11, runs on Linux (fully validated) with partial Windows/macOS support, and uses PyTorch with optional integrations like OpenEye ROCS and Chemprop v2.
Get the REINVENT4 source
Clone the repository and explore it locally.
git clone https://github.com/MolecularAI/REINVENT4.gitcd REINVENT4# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- GPU setup is critical path: PyTorch configuration must match OS driver (NVIDIA CUDA versions, AMD ROCm, Intel XPU, Apple MPS). Mismatch will cause silent fallback to CPU with dramatic slowdown.
- Scoring component design dictates results quality. Default internal priors available via dot notation, but domain-specific objectives require custom Python plugins following @add_tag decorator pattern and namespace package conventions.
- Configuration files (TOML/JSON/YAML) are mandatory; no Python API documented. Site-specific paths, model selection, and run mode (sampling vs. RL vs. TL) must be hand-authored from templates in /configs.
- Transfer learning requires curated input molecule sets; pre-trained prior models are available on Zenodo but selection and fine-tuning strategy are user responsibility.
- Jupyter notebooks provided in jupytext format; requires jupytext + mols2grid + seaborn for interactive exploration; conversion to standard .ipynb needed.
When to avoid it — and what to weigh
- Rigorous regulatory compliance auditing required — No explicit information on validation pipelines, model reproducibility certifications, or FDA 21 CFR Part 11 compliance. Requires internal governance review before clinical-stage adoption.
- Windows/macOS as primary production platform — Linux is the fully validated environment. Windows support is documented as 'less well tested' with 'limited support.' macOS GPU support depends on Apple Silicon adoption and has unspecified stability guarantees.
- Immediate out-of-the-box inference at scale — Requires environment setup (conda/uv, PyTorch tuning for GPU/CPU, CUDA/ROCm/XPU compatibility), configuration file authoring (TOML/JSON/YAML), and custom scoring plugins for domain-specific objectives. Not a zero-config service.
- Low computational overhead or CPU-only deployments — GPU strongly recommended, especially for transfer learning. CPU-only runs are possible but performance characteristics undefined. ~8 GB CPU/GPU memory typical; RL scoring offloaded to CPU may bottleneck.
License & commercial use
Apache License 2.0 (Apache-2.0). Permissive OSI-approved license allowing commercial use, modification, and distribution with standard Apache 2.0 terms (attribution, liability disclaimer, patent grant).
Apache 2.0 explicitly permits commercial use without runtime fees or license agreements. However, optional third-party dependencies (OpenEye ROCS) require separate commercial licenses. No internal constraints on derivative works or proprietary use; standard Apache 2.0 patent indemnity and IP clearance provisions apply. Internal governance should review if integrating proprietary scoring components or deploying as a commercial service.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | High |
| DEV.co fit | Good |
| Assessment confidence | High |
No explicit security audit, threat model, or vulnerability disclosure process documented. Python dependencies and PyTorch versions inherit upstream security posture; pin versions per your governance. CLI accepts arbitrary TOML/JSON/YAML and environment variables (dotenv file); validate configuration inputs if accepting untrusted user configurations. No mention of code signing, reproducible builds, or container image provenance.
Alternatives to consider
DeepChem (UC Berkeley, OSS)
General-purpose deep learning for molecular property prediction and generation. Broader ML toolkit but less specialized for de novo design workflows; weaker RL/TL integration.
RDKit + custom RL (in-house)
RDKit for cheminformatics + DIY PyTorch/TensorFlow RL loop. Full control but requires internal expertise; slower to production than pre-built REINVENT4.
Proprietary/SaaS tools (e.g., Schrödinger, Exscientia platforms)
Managed, validated, with commercial support. Higher cost and vendor lock-in; less transparency than open-source REINVENT4.
Build on REINVENT4 with DEV.co software developers
REINVENT4 is a battle-tested, Apache 2.0-licensed platform backed by Astra Zeneca research. Start with our tutorials and pre-trained models on Zenodo. Contact us to discuss integration into your drug discovery workflow.
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REINVENT4 FAQ
Do I need a GPU?
Which GPUs are supported?
Can I use REINVENT4 on Windows or macOS?
How do I add custom scoring objectives?
Custom software development services
DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If REINVENT4 is part of your ai frameworks roadmap, our team can implement, customize, migrate, and maintain it.
Ready to accelerate your molecular design pipeline?
REINVENT4 is a battle-tested, Apache 2.0-licensed platform backed by Astra Zeneca research. Start with our tutorials and pre-trained models on Zenodo. Contact us to discuss integration into your drug discovery workflow.