REINVENT 2.0: An AI Tool for De Novo Drug Design
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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.
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Mature software tools now exist to run generative models on conventional computers, and community-accepted benchmarks enable systematic assessment of generated small molecules considering predicted activity, novelty, validity, and diversity.
“Mature software tools now exist to run generative models on conventional computers,154,157 and community-accepted benchmarks enable the systematic assessment of generated small-molecules, considering not only their predicted activity but also their novelty, validity, and diversity.158,159”
From Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence (2023) · cites this paper as [154]
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- Authors
- Blaschke, T., Arús-Pous, J., Chen, H., Margreitter, C., Tyrchan, C., Engkvist, O., Papadopoulos, K., & Patronov, A.
- Affiliations
- AstraZeneca (Sweden) (SE)
- AstraZeneca , , Pepparedsleden 1 , ,
- Guangzhou Regenerative Medicine and Health Guangdong Laboratory (CN)
- Corresponding author
- Patronov, Atanas
- ORCID
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2020-10-29
- Volume
- 60
- Issue
- 12
- Pages
- 5918-5922
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W3094686696
- Cited by
- 496
- References
- 38
- Citations by year
- 2026: 98 · 2025: 88 · 2024: 123 · 2023: 74 · 2022: 50 · 2021: 56 · 2020: 7
- FWCI
- 23.35
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Journal of Chemical Information and Modeling (publishedVersion)
- ChemRxiv (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Figshare (submittedVersion)
- Funders
- European Commission (HORIZON2020, 676434)
- Research Executive Agency
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Degradation and Inhibitors
- Keywords
- de novo drug design, REINVENT, generative AI, chemical space exploration, graph representation, drug discovery, open source software
- MeSH terms
- Artificial Intelligence, Drug Design, Drug Discovery
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