Scalable and Generalizable Analog Design via Learning Medicinal Chemistry Intuition from Matched Molecular Pair Transformations.
- DOI
- 10.1021/acs.jcim.6c01032
- Published
- 2026 Aug 10
- Container
- Journal of chemical information and modeling
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1021/acs.jcim.6c01032,
title = {Scalable and Generalizable Analog Design via Learning Medicinal Chemistry Intuition from Matched Molecular Pair Transformations.},
author = {Pang HW and Zhang PZ and Pan B and Zhao L and Yu X and Zhang L},
year = {2026},
journal = {Journal of chemical information and modeling},
doi = {10.1021/acs.jcim.6c01032},
url = {https://doi.org/10.1021/acs.jcim.6c01032}
}RIS
TY - JOUR TI - Scalable and Generalizable Analog Design via Learning Medicinal Chemistry Intuition from Matched Molecular Pair Transformations. AU - Pang HW AU - Zhang PZ AU - Pan B AU - Zhao L AU - Yu X AU - Zhang L PY - 2026 JO - Journal of chemical information and modeling DO - 10.1021/acs.jcim.6c01032 UR - https://doi.org/10.1021/acs.jcim.6c01032 ER -
APA
HW, P., PZ, Z., B, P., L, Z., X, Y., & L, Z. (2026). Scalable and Generalizable Analog Design via Learning Medicinal Chemistry Intuition from Matched Molecular Pair Transformations.. Journal of chemical information and modeling. https://doi.org/10.1021/acs.jcim.6c01032
Source records
- pubmed · retrieved 2026-09-25T15:14:47.764Z