The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
The importance of synthetic chemistry in the pharmaceutical industry
Abstract
Synthetic innovation in drug development: Chemical synthesis plays a key role in pharmaceutical research and development. Campos et al. review some of the advantages that have come from recent innovations in synthetic methods. In particular, they highlight small-molecule catalysts stimulated by visible light, enzymes engineered for versatility beyond their intrinsic function, and bio-orthogonal reactions to selectively modify proteins for conjugation. High-throughput techniques are also poised to accelerate methods optimization from small-scale discovery to large-scale production, and complementary machine-learning approaches are just coming into focus. Science , this issue p. eaat0805
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Predicting reaction performance in C–N cross-coupling using machine learning
Ahneman, D. T., Estrada, J. G., Lin, S., Dreher, S. D., & Doyle, A. G. (2018) · Science · doi:10.1126/science.aar5169 · Cited by 1,058
Self-citationRetrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models
Liu, B., Ramsundar, B., Kawthekar, P., Shi, J., Gomes, J., Luu Nguyen, Q., Ho, S., Sloane, J., Wender, P., & Pande, V. (2017) · ACS Central Science · doi:10.1021/acscentsci.7b00303 · Cited by 536
Cited by
Structure-Based Drug Design with a Deep Hierarchical Generative Model
Weller, J. A., & Rohs, R. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01193 · Cited by 47
Metadata
- Authors
- Campos, K. R., Coleman, P. J., Alvarez, J. C., Dreher, S. D., Garbaccio, R. M., Terrett, N. K., Tillyer, R. D., Truppo, M. D., & Parmee, E. R.
- Venue
- Science
- Published
- 2019-01-18
- Volume
- 363
- Issue
- 6424
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2909929882
- Cited by
- 668
- References
- 2
- FWCI
- 14.61
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: The importance of synthetic chemistry in the pharmaceutical industry. https://app.cochat.ai/papers/10.1126/science.aat0805
- The paper
- Campos, K. R., Coleman, P. J., Alvarez, J. C., Dreher, S. D., Garbaccio, R. M., Terrett, N. K., Tillyer, R. D., Truppo, M. D., & Parmee, E. R. (2019). The importance of synthetic chemistry in the pharmaceutical industry. Science. https://doi.org/10.1126/science.aat0805
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.