Retro Drug Design: From Target Properties to Molecular Structures
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Abstract
To deliver more therapeutics to more patients more quickly and economically is the ultimate goal of pharmaceutical researchers. The advent and rapid development of artificial intelligence (AI), in combination with other powerful computational methods in drug discovery, makes this goal more practical than ever before. Here, we describe a new strategy, retro drug design, or RDD, to create novel small-molecule drugs from scratch to meet multiple predefined requirements, including biological activity against a drug target and optimal range of physicochemical and ADMET properties. The molecular structure was represented by an atom typing based molecular descriptor system, optATP, which was further transformed to the space of loading vectors from principal component analysis. Traditional predictive models were trained over experimental data for the target properties using optATP and shallow machine learning methods. The Monte Carlo sampling algorithm was then utilized to find the solutions in the space of loading vectors that have the target properties. Finally, a deep learning model was employed to decode molecular structures from the solutions. To test the feasibility of the algorithm, we challenged RDD to generate novel kinase inhibitors from random numbers with five different ADMET properties optimized at the same time. The best Tanimoto similarity score between the generated valid structures and the available 4,314 kinase inhibitors was < 0.50, indicating a high extent of novelty of the generated compounds. From the 3,040 structures that met all six target properties, 20 were selected for synthesis and experimental measurement of inhibition activity over 97 representative kinases and the ADMET properties. Fifteen and eight compounds were determined to be hits or strong hits, respectively. Five of the six strong kinase inhibitors have excellent experimental ADMET properties. The results presented in this paper illustrate that RDD has the potential to significantly improve the current drug discovery process.
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- Authors
- Wang, Y., Michael, S., Yang, S.-M., Huang, R., Cruz-Gutierrez, K., Zhang, Y., Zhao, J., Xia, M., Shinn, P., & Sun, H.
- Affiliations
- National Center for Advancing Translational Sciences (US)
- Corresponding author
- Wang, Yuhong
- Sun, Hongmao
- ORCID
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2022-06-02
- Volume
- 62
- Issue
- 11
- Pages
- 2659-2669
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- PMC
- PMC9198977
- OpenAlex
- W4281699348
- Cited by
- 24
- References
- 60
- Citations by year
- 2026: 5 · 2025: 7 · 2024: 5 · 2023: 3 · 2022: 3 · 2021: 1
- FWCI
- 0.41
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Chemical Information and Modeling (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- National Center for Advancing Translational Sciences
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Chemistry › Organic Chemistry › Synthesis and Characterization of Heterocyclic Compounds
- Keywords
- small-molecule drug design, atom typing, principal component analysis, Monte Carlo sampling, kinase inhibitors, ADMET properties
- MeSH terms
- Machine Learning, Artificial Intelligence, Humans, Drug Design, Molecular Structure, Drug Discovery
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