Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-14T10:20:02.734496Z
Paper Citation Record · LEDGER
PaccMann$^{RL}$: Designing anticancer drugs from transcriptomic data via reinforcement learning
As of 20 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:1909.05114.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
23 of 23 outbound references displayed
- verified exact7
- verified fuzzy7
- unresolved9
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 418920c0-b9c4-4803-985d-c277fa8f680d · outbound
This paper cites The model was trained with a batch size of 64 for a maximum of 2000 epochs.
PaccMann$^{RL}$: Designing anticancer drugs from transcriptomic data via reinforcement learning The model was trained with a batch size of 64 for a maximum of 2000 epochs
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:20:03.017549Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 7e58953f-9f80-4575-9c39-6b8d6711519d · outbound
This paper cites an unresolved cited work.
PaccMann$^{RL}$: Designing anticancer drugs from transcriptomic data via reinforcement learning Unresolved cited work
Reference 5
- Resolution
- unresolved
- raw_fallback, observed 2026-08-14T10:20:02.990808Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation a06d3168-076a-414b-a13f-8e9a7c62dafd · outbound
This paper cites Juan Lao, Julia Madani, Teresa Pu´ ertolas, Mar´ ıa´Alvarez, Alba Hern´ andez, Roberto Pazo-Cid,´Angel Artal, and Antonio Ant´ on Torres.
PaccMann$^{RL}$: Designing anticancer drugs from transcriptomic data via reinforcement learning Juan Lao, Julia Madani, Teresa Pu´ ertolas, Mar´ ıa´Alvarez, Alba Hern´ andez, Roberto Pazo-Cid,´Angel Artal, and Antonio Ant´ on Torres
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:20:03.072517Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 4907c644-6dcb-4129-a8d7-2fcff8dd8c00 · outbound
This paper cites Visualization of Very Large High-Dimensional Data Sets as Minimum Spanning Trees.
PaccMann$^{RL}$: Designing anticancer drugs from transcriptomic data via reinforcement learning Visualization of Very Large High-Dimensional Data Sets as Minimum Spanning Trees
Reference 10
- Resolution
- verified exact
- local_arxiv, observed 2026-08-14T10:20:02.878735Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation c7a7f844-03f0-4865-9d09-6f74c9d99f1e · outbound
This paper cites Ketan T Savjani, Anuradha K Gajjar, and Jignasa K Savjani.
PaccMann$^{RL}$: Designing anticancer drugs from transcriptomic data via reinforcement learning Ketan T Savjani, Anuradha K Gajjar, and Jignasa K Savjani
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:20:03.059269Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 7f0959ce-adfa-4a20-8216-a449010dd097 · outbound
This paper cites URL https://www.biorxiv.org/content/early/2020/01/25/2020.01.24.918953.
PaccMann$^{RL}$: Designing anticancer drugs from transcriptomic data via reinforcement learning URL https://www.biorxiv.org/content/early/2020/01/25/2020.01.24.918953
Reference 13
- Resolution
- verified exact
- doi, observed 2026-08-14T10:20:02.797323Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 6ef2a519-4ddc-4b17-8cc1-8a04fc42ca41 · outbound
This paper cites Gradient Surgery for Multi-Task Learning.
PaccMann$^{RL}$: Designing anticancer drugs from transcriptomic data via reinforcement learning Gradient Surgery for Multi-Task Learning
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T10:20:02.702228Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c3c4cf23-1aff-4cc0-bba6-b9217678fec5 · outbound
This paper cites XQ Zhang, CY Yang, XF Rao, and JP Xiong.
PaccMann$^{RL}$: Designing anticancer drugs from transcriptomic data via reinforcement learning XQ Zhang, CY Yang, XF Rao, and JP Xiong
Reference 16
- Resolution
- verified exact
- doi, observed 2026-08-14T10:20:02.769619Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation e8565dae-df55-4f2b-85f6-677936cc103d · outbound
This paper cites an unresolved cited work.
PaccMann$^{RL}$: Designing anticancer drugs from transcriptomic data via reinforcement learning Unresolved cited work
Reference 17
- Resolution
- unresolved
- raw_fallback, observed 2026-08-14T10:20:03.043572Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation a0d98288-5e7d-413f-870a-161f6639d8a8 · outbound
This paper cites #"$! % % !.
PaccMann$^{RL}$: Designing anticancer drugs from transcriptomic data via reinforcement learning #"$! % % !
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:20:02.977973Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 6aeb572c-2b04-4680-a9c6-a1b11172ee93 · outbound
This paper cites The predicted synthesis consists of four sequential reactions with a total 10 commercially available reactants (green).
PaccMann$^{RL}$: Designing anticancer drugs from transcriptomic data via reinforcement learning The predicted synthesis consists of four sequential reactions with a total 10 commercially available reactants (green)
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:20:02.965294Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 849985d1-d2e6-4fcd-ba54-9697619ffdb8 · outbound
This paper cites Similar optimization parameters as PVAE were used.
PaccMann$^{RL}$: Designing anticancer drugs from transcriptomic data via reinforcement learning Similar optimization parameters as PVAE were used
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:20:03.004932Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation fb85ea09-a967-4b2c-b0ba-f41ab7c166ac · outbound
This paper cites To further regularize the PVAE, denoising methods were employed by.
PaccMann$^{RL}$: Designing anticancer drugs from transcriptomic data via reinforcement learning To further regularize the PVAE, denoising methods were employed by
Reference 128
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T10:20:03.030673Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 2e1b845b-0342-4246-bcb1-957a336596c7 · outbound
This paper cites Learning to SMILE(S).
PaccMann$^{RL}$: Designing anticancer drugs from transcriptomic data via reinforcement learning Learning to SMILE(S)
Reference 1969
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T10:20:02.657259Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ceb3bfd0-f405-492c-807f-02e268af5349 · outbound
This paper cites Joaquin Dopazo.
PaccMann$^{RL}$: Designing anticancer drugs from transcriptomic data via reinforcement learning Joaquin Dopazo
Reference 2004
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T10:20:02.652646Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation db599ce1-a1a3-4733-8822-ea975531ce80 · outbound
This paper cites David Weininger.
PaccMann$^{RL}$: Designing anticancer drugs from transcriptomic data via reinforcement learning David Weininger
Reference 2009
- Resolution
- verified exact
- doi, observed 2026-08-14T10:20:02.781969Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 23c11aa5-3ef9-4cad-bdcc-c14e33032a24 · outbound
This paper cites doi: 10.1021/ci100050t.
PaccMann$^{RL}$: Designing anticancer drugs from transcriptomic data via reinforcement learning doi: 10.1021/ci100050t
Reference 2010
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T10:20:02.685542Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2ec1567a-edba-469b-8f89-b2844db3b1d2 · outbound
This paper cites doi: 10.1093/nar/gkt1031.
PaccMann$^{RL}$: Designing anticancer drugs from transcriptomic data via reinforcement learning doi: 10.1093/nar/gkt1031
Reference 2013
- Resolution
- verified exact
- doi, observed 2026-08-14T10:20:02.846251Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation c41f427b-19ef-4a21-9f69-3c43b4f0d2e9 · outbound
This paper cites Auto-Encoding Variational Bayes.
PaccMann$^{RL}$: Designing anticancer drugs from transcriptomic data via reinforcement learning Auto-Encoding Variational Bayes
Reference 2014
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T10:20:02.667791Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 93d254ba-3a5a-4053-a75e-637af08ff92a · outbound
This paper cites Adam: A Method for Stochastic Optimization.
PaccMann$^{RL}$: Designing anticancer drugs from transcriptomic data via reinforcement learning Adam: A Method for Stochastic Optimization
Reference 2017
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T10:20:02.662317Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cba6bc53-5611-42f4-8208-a0db28429b9c · outbound
This paper cites Generating Sentences from a Continuous Space.
PaccMann$^{RL}$: Designing anticancer drugs from transcriptomic data via reinforcement learning Generating Sentences from a Continuous Space
Reference 2018
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T10:20:02.648171Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 98fbba62-07fd-4736-81ed-2d7e342064eb · outbound
This paper cites PMID: 31618586.
PaccMann$^{RL}$: Designing anticancer drugs from transcriptomic data via reinforcement learning PMID: 31618586
Reference 2019
- Resolution
- verified exact
- doi, observed 2026-08-14T10:20:02.823984Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation d634367f-901d-454a-a569-fef2aa434f02 · outbound
This paper cites Latent Molecular Optimization for Targeted Therapeutic Design.
PaccMann$^{RL}$: Designing anticancer drugs from transcriptomic data via reinforcement learning Latent Molecular Optimization for Targeted Therapeutic Design
Reference 2020
- Resolution
- verified exact
- local_arxiv, observed 2026-08-14T10:20:02.952133Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Pith citing papers
No inbound Pith citation observations are available.