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Characterizing emerging companies in computational drug development

Nature Computational Science volume 4, pages 96–103 (2024)Cite this article

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Abstract

Computation promises to accelerate, de-risk and optimize drug research and development. An increasing number of companies have entered this space, specializing in the design of new algorithms, computing on proprietary data, and/or development of hardware to improve distinct drug pipeline stages. The large number of such companies and their unique strategies and deals have created a highly complex and competitive industry. We comprehensively analyze the companies in this space to highlight trends and opportunities, identifying highly occupied areas of risk and currently underrepresented niches of high value.

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Fig. 1: Breakdown of computational biotech company/pipeline ownership.
Fig. 2: Company-differentiating positioning and average funding on the basis of drug R&D focus and computational capabilities.
Fig. 3: Timelines of company, deal and funding accumulation.
Fig. 4: Distribution of deal type for computational companies with a drug pipeline.
Fig. 5: Computational biotech deal network analysis.
Fig. 6: Current computational pipeline analysis.

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Data availability

The data used for this analysis were retrieved from Cortellis (https://access.clarivate.com/login?app=cortellis) and Crunchbase (https://www.crunchbase.com/). Accounts with these providers are required to access their data. To ensure data availability for the work provided here, the curated data used to generate our figures and analysis are available on GitHub at https://github.com/RekerLab/ComputationalDrugRD and on Zenodo at https://doi.org/10.5281/zenodo.10482006 ref. 32.

Code availability

The code used to generate our figures and analysis are available on GitHub at https://github.com/RekerLab/ComputationalDrugRD and on Zenodo at https://doi.org/10.5281/zenodo.10482006 ref. 32.

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Acknowledgements

This work was supported by the Duke Science and Technology Initiative and the NIH NIGMS grant R35GM151255 (D.R.). C.M. is supported by a Pratt Gardner Fellowship from the Duke Biomedical Engineering Department and by the NIH NIGMS Pharmacological Sciences Training Grant NIH T32 GM133352-4.

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Author notes

  1. These authors contributed equally: Chloe Markey, Samuel Croset.

Authors and Affiliations

  1. Department of Biomedical Engineering, Duke University, Durham, NC, USA

    Chloe Markey & Daniel Reker

  2. Bellevue Asset Management, Zurich, Switzerland

    Samuel Croset, Olivia Ruth Woolley, Can Martin Buldun, Christian Koch & Daniel Koller

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  1. Chloe Markey
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Contributions

C.M., S.C., C.K., D.K. and D.R. conceived the study and designed experiments. C.M. and S.C. conducted experiments. C.M., S.C., O.R.W., C.M.B, C.K., D.K. and D.R. analyzed the data. C.M., S.C., C.K., D.K. and D.R. wrote the manuscript. C.K., D.K. and D.R. supervised the work. All authors have read and approved the submitted version of the manuscript.

Corresponding authors

Correspondence to Christian Koch, Daniel Koller or Daniel Reker.

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Competing interests

D.R. acts as a consultant to the pharmaceutical and biotechnology industry, as a scientific mentor for the German Accelerator, and serves on the scientific advisory board of Areteia Therapeutics. S.C., O.R.W., C.M.B., C.K. and D.K. are employees of Bellevue Asset Management and members of the investment team BB Biotech, with investments in the biotechnology industry including some of the companies analyzed here—a full list of the portfolio is available at https://www.bbbiotech.ch/ch-en/private/portfolio-strategy/performance-portfolio/our-investments.

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Peer review information

Nature Computational Science thanks Andreas Bender and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Kaitlin McCardle, in collaboration with the Nature Computational Science team.

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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Tables 1–3 and Figs. 1–4.

Supplementary Data 1

The companies profiled in this analysis. The sheet shows the company reference list used to conduct the analysis. The data in the value matrix column refer to the type of advantage the company had. The letter indicates the R&D part and the digit the computational advantage. For the R&D component, the indexing is the following: A, modality; B, biology; C, delivery; D, safety; E, clinical; F, regulatory. For the computational advantage, the index is the following: 1, hardware; 2, dataset; 3, algorithm (XLSX 14 kb).

Supplementary Data 2

The projects analyzed in this paper. The sheet shows the most advanced projects in computational drug R&D company pipelines (XLSX 11 kb).

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Markey, C., Croset, S., Woolley, O.R. et al. Characterizing emerging companies in computational drug development. Nat Comput Sci 4, 96–103 (2024). https://doi.org/10.1038/s43588-024-00594-8

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