Paper Citation Record · LEDGER

Enhancing Molecular Design through Graph-based Topological Reinforcement Learning

As of 13 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2411.14726.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2411.14726 v1

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T15:02:34.866461Z

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

15 of 15 outbound references displayed

verified exact2
verified fuzzy11
unresolved2
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5535e547-174c-4659-b6a5-8bd80ef78a35 · outbound

This paper cites Deep reinforcement learning for multiparameter optimization in de novo drug design.

Enhancing Molecular Design through Graph-based Topological Reinforcement Learning Deep reinforcement learning for multiparameter optimization in de novo drug design

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:02:34.998903Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 62de980c-0f1e-4021-8404-8b0c93cd667a · outbound

This paper cites Molecular de-novo design through deep reinforcement learning.

Enhancing Molecular Design through Graph-based Topological Reinforcement Learning Molecular de-novo design through deep reinforcement learning

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:02:34.991795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 7277186e-8662-450c-a631-f4e056d1a7ba · outbound

This paper cites Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models.

Enhancing Molecular Design through Graph-based Topological Reinforcement Learning Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-12T15:02:34.832329Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e27876de-928f-4e0a-aeeb-b33b3b89294d · outbound

This paper cites Reinforced adversarial neural computer for de novo molecular design.

Enhancing Molecular Design through Graph-based Topological Reinforcement Learning Reinforced adversarial neural computer for de novo molecular design

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:02:34.984728Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 8e1aab7b-7eea-46e0-827e-710d347ce316 · outbound

This paper cites Optimization of molecules via deep reinforcement learning.

Enhancing Molecular Design through Graph-based Topological Reinforcement Learning Optimization of molecules via deep reinforcement learning

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:02:34.977688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c8ada2ea-3c0d-46ed-beda-e5c476df12b7 · outbound

This paper cites Eisa-score: Element interactive surface area score for protein–ligand binding affinity prediction.

Enhancing Molecular Design through Graph-based Topological Reinforcement Learning Eisa-score: Element interactive surface area score for protein–ligand binding affinity prediction

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:02:34.970422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 36f5c731-9be8-409d-b58f-156259ada9aa · outbound

This paper cites Persistent Directed Flag Laplacian (PDFL)-Based Machine Learning for Protein-Ligand Binding Affinity Prediction.

Enhancing Molecular Design through Graph-based Topological Reinforcement Learning Persistent Directed Flag Laplacian (PDFL)-Based Machine Learning for Protein-Ligand Binding Affinity Prediction

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-08-12T15:02:34.902633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 2732eede-cd09-47b5-a120-a04c7c3613fa · outbound

This paper cites A review of geometric, topological and graph theory apparatuses for the modeling and analysis of biomolecular data.

Enhancing Molecular Design through Graph-based Topological Reinforcement Learning A review of geometric, topological and graph theory apparatuses for the modeling and analysis of biomolecular data

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-08-12T15:02:34.892010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f047e6c0-d102-44a2-97ac-29d4707a6b10 · outbound

This paper cites Geometric graph learning with extended atom-types features for protein-ligand binding affinity prediction.

Enhancing Molecular Design through Graph-based Topological Reinforcement Learning Geometric graph learning with extended atom-types features for protein-ligand binding affinity prediction

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:02:34.962883Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 48a59b9c-b884-40a9-ade3-26948da1e361 · outbound

This paper cites an unresolved cited work.

Enhancing Molecular Design through Graph-based Topological Reinforcement Learning Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-08-12T15:02:34.955542Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a542fb16-1ccd-4bc1-8c5a-c92f4b0c9aa6 · outbound

This paper cites Fast and anisotropic flexibility-rigidity index for protein flexibility and fluctuation analysis.

Enhancing Molecular Design through Graph-based Topological Reinforcement Learning Fast and anisotropic flexibility-rigidity index for protein flexibility and fluctuation analysis

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:02:34.948019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation d2ea5447-4a3a-4b33-a6d8-7c97c1e7a3fa · outbound

This paper cites A value-based deep reinforcement learning model with human expertise in optimal treatment of sepsis.

Enhancing Molecular Design through Graph-based Topological Reinforcement Learning A value-based deep reinforcement learning model with human expertise in optimal treatment of sepsis

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:02:34.940523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation cd62daf2-af24-4401-8806-7449e097fdda · outbound

This paper cites Junction tree variational autoencoder for molecular graph generation.

Enhancing Molecular Design through Graph-based Topological Reinforcement Learning Junction tree variational autoencoder for molecular graph generation

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:02:34.932938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 54dd6236-9ff5-4210-81d5-8b70da05e3c4 · outbound

This paper cites Mol-cyclegan: a generative model for molecular optimization.

Enhancing Molecular Design through Graph-based Topological Reinforcement Learning Mol-cyclegan: a generative model for molecular optimization

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:02:34.925425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 8d8689bd-033e-4c14-a54f-52639e80bca9 · outbound

This paper cites Zinc: a free tool to discover chemistry for biology.

Enhancing Molecular Design through Graph-based Topological Reinforcement Learning Zinc: a free tool to discover chemistry for biology

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:02:34.917665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Pith citing papers

No inbound Pith citation observations are available.