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Link-INVENT: Generative Linker Design with Reinforcement Learning
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In this work, we present Link-INVENT as an extension to the existing de novo molecular design platform REINVENT. We provide illustrative examples on how Link-INVENT can be applied on fragment linking, scaffold hopping, and PROTACs design case studies where the desirable molecules should satisfy a combination of different criteria. With the help of Reinforcement Learning, the agent used by Link-INVENT learns to generate favourable linkers connecting molecular subunits that satisfy diverse objectives, facilitating practical application of the model for real-world drug discovery projects. We also introduce a range of linker-specific objectives in the scoring function of REINVENT. The code is freely available at https://github.com/MolecularAI/Reinvent.
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![Fig. 10 Experiment 3: PROTAC Link-INVENT results. The experiments were run in triplicate. The curve in (c) shows the average score achieved by the batch of molecules sampled at a given epoch and the upper and lower bounds of the shaded region represent the maximum and minimum scores, respectively. (a) Experiment that fi xes physico-chemical properties and tasks Link-INVENT with generating linkers with an e ff ective length within the speci fi ed intervals: [4, 6], [7, 9], [10, 12], and [13, 15]. The baseline experiment does not enforce the linker length. Consequently, the e ff ective linker lengths resemble a broad distribution. The generated linkers from the triplicate runs which also satisfy all the physico-chemical property criteria are aggregated in the plot. (b) Experiment that fi xes physico-chemical properties and the e ff ective linker length within the interval [7 -9]. Link-INVENT is tasked with generating linear and cyclic linkers. The baseline experiment does not enforce linker linearity and approximately a 1 : 2 ratio of linear : cyclic is observed. The generated linkers from the triplicate runs which also satisfy all the physico-chemical property criteria are aggregated in the plot. (c) Experiment that tasks Link-INVENT with generating linkers containing a ratio of rotatable bonds within the speci fi ed intervals: ' Low ' [0, 30], ' Moderate ' [40, 60], and ' High ' [70, 100]. The agent implicitly learns that linkers containing rings and sp 2 hybridized atoms achieve a low ratio of rotatable bonds. Conversely, linear linkers with sp 3 hybridized atoms achieve a high ratio of rotatable bonds.](/paper-figures/81865bb0216d483790c47ecba2259f59/link-invent-generative-linker-design-with-reinforcement-learning-figure-10.png)
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“Moreover, existing platforms have been versioned to design optimal linkers connecting the two molecular subunits in a PROTAC.163”
From Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence (2023) · cites this paper as [163]
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Metadata
- Authors
- Guo, J., Knuth, F., Margreitter, C., Janet, J. P., Papadopoulos, K., Engkvist, O., & Patronov, A.
- Affiliations
- AstraZeneca (Sweden) (SE)
- Norwegian University of Science and Technology (NO)
- Corresponding author
- Patronov, Atanas
- ORCID
- Venue
- Digital Discovery
- Publisher
- American Chemical Society (ACS)
- Published
- 2022-04-24
- Volume
- 2
- Issue
- 2
- Pages
- 392-408
- ISSN
- 2635-098X (ISSN-L)
- OpenAlex
- W4319164354
- Cited by
- 101
- References
- 62
- Citations by year
- 2026: 30 · 2025: 29 · 2024: 26 · 2023: 15 · 2022: 1
- FWCI
- 14.46
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Digital Discovery (publishedVersion, cc-by)
- ChemRxiv (acceptedVersion, cc-by)
- Chalmers Research (Chalmers University of Technology) (submittedVersion)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Degradation and Inhibitors
- Physical Sciences › Chemistry › Organic Chemistry › Chemical Synthesis and Analysis
- Keywords
- PROTACs, linker design, fragment linking, scaffold hopping, generative design, reinforcement learning
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