The DOI matches its Crossref record; 76 of the 76 references it cites were found in a registry or an index.
Reinvent 4: Modern AI–driven generative molecule design
Open accessAbstract
REINVENT 4 is a modern open-source generative AI framework for the design of small molecules. The software utilizes recurrent neural networks and transformer architectures to drive molecule generation. These generators are seamlessly embedded within the general machine learning optimization algorithms, transfer learning, reinforcement learning and curriculum learning. REINVENT 4 enables and facilitates de novo design, R-group replacement, library design, linker design, scaffold hopping and molecule optimization. This contribution gives an overview of the software and describes its design. Algorithms and their applications are discussed in detail. REINVENT 4 is a command line tool which reads a user configuration in either TOML or JSON format. The aim of this release is to provide reference implementations for some of the most common algorithms in AI based molecule generation. An additional goal with the release is to create a framework for education and future innovation in AI based molecular design. The software is available from https://github.com/MolecularAI/REINVENT4 and released under the permissive Apache 2.0 license.Scientific contribution. The software provides an open–source reference implementation for generative molecular design where the software is also being used in production to support in–house drug discovery projects. The publication of the most common machine learning algorithms in one code and full documentation thereof will increase transparency of AI and foster innovation, collaboration and education.
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Trott, O., & Olson, A. J. (2010) · Journal of Computational Chemistry · doi:10.1002/jcc.21334 · Cited by 38,412
Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (2001) · Advanced Drug Delivery Reviews · doi:10.1016/s0169-409x(00)00129-0 · Cited by 18,139
Reinforcement Learning: An Introduction
Johnson, J. D., Li, J., & Chen, Z. (2000) · Neurocomputing · doi:10.1016/s0925-2312(00)00324-6 · Cited by 8,414
AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings
Eberhardt, J., Santos-Martins, D., Tillack, A. F., & Forli, S. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c00203 · Cited by 7,546
Friesner, R. A., Murphy, R. B., Repasky, M. P., Frye, L. L., Greenwood, J. R., Halgren, T. A., Sanschagrin, P. C., & Mainz, D. T. (2006) · Journal of Medicinal Chemistry · doi:10.1021/jm051256o · Cited by 6,851
Development and validation of a genetic algorithm for flexible docking 1 1Edited by F. E. Cohen
Jones, G., Willett, P., Glen, R. C., Leach, A. R., & Taylor, R. (1997) · Journal of Molecular Biology · doi:10.1006/jmbi.1996.0897 · Cited by 6,763
Error bounds for convolutional codes and an asymptotically optimum decoding algorithm
Viterbi, A. (1967) · IEEE Transactions on Information Theory · doi:10.1109/tit.1967.1054010 · Cited by 6,759
A Learning Algorithm for Continually Running Fully Recurrent Neural Networks
Williams, R. J., & Zipser, D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.2.270 · Cited by 4,369
Kim, S., Chen, J., Cheng, T., Gindulyte, A., He, J., He, S., Li, Q., Shoemaker, B. A., Thiessen, P. A., Yu, B., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkac956 · Cited by 3,443
Self-citationAutomatic Chemical Design Using a Data-Driven Continuous Representation of Molecules
Gómez-Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., Aguilera-Iparraguirre, J., Hirzel, T. D., Adams, R. P., & Aspuru-Guzik, A. (2018) · ACS Central Science · doi:10.1021/acscentsci.7b00572 · Cited by 3,106
Cited by
Self-Driving Laboratories for Chemistry and Materials Science
Tom, G., Schmid, S. P., Baird, S. G., Cao, Y., Darvish, K., Hao, H., Lo, S., Pablo-García, S., Rajaonson, E. M., Skreta, M., et al. (2024) · Chemical Reviews · doi:10.1021/acs.chemrev.4c00055 · Cited by 707
Unleashing the power of generative AI in drug discovery
Gangwal, A., & Lavecchia, A. (2024) · Drug Discovery Today · doi:10.1016/j.drudis.2024.103992 · Cited by 195
Democratising real-world drug discovery through agentic AI
He, J., Lai, H., Saigiridharan, L., Ghiandoni, G. M., Jenei, K., Gokalp, U., Nuković, A., Engkvist, O., Janet, J. P., & Genheden, S. (2026) · Drug Discovery Today · doi:10.1016/j.drudis.2026.104605 · Cited by 17
Self-citationPalmacci, V., Nahal, Y., Welsch, M., Engkvist, O., Kaski, S., & Kirchmair, J. (2025) · Journal of Cheminformatics · doi:10.1186/s13321-025-01014-3 · Cited by 6
Self-citationLarge Language Model Agent for Modular Task Execution in Drug Discovery
Ock, J., Meda, R. S., Badrinarayanan, S., Aluru, N. S., Chandrasekhar, A., & Barati Farimani, A. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c02454 · Cited by 5
Metadata
- Authors
- Loeffler, H. H., He, J., Tibo, A., Janet, J. P., Voronov, A., Mervin, L. H., & Engkvist, O.
- Venue
- Journal of Cheminformatics
- Published
- 2024-02-21
- Volume
- 16
- Issue
- 1
- Pages
- 20-20
- ISSN
- 1758-2946 (ISSN-L)
- OpenAlex
- W4392015344
- Cited by
- 319
- References
- 76
- FWCI
- 62.54
- Type
- journal article
- Language
- en
- Open access
- gold
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