De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning | Paper Citation Record

Observation eb7e68e9-610a-444a-9dda-da0739877316 · outbound

This paper cites Identiﬁcation of drug-targ et interac- tions via multi-view graph regularized link propagation mo del,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning Identiﬁcation of drug-targ et interac- tions via multi-view graph regularized link propagation mo del,

Reference 1

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This paper cites ChemoGraph: Interactive visual exploration o f the chemical space,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning ChemoGraph: Interactive visual exploration o f the chemical space,

Reference 2

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De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning Unresolved cited work

Reference 3

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Observation 85b0eeff-d12b-4a37-b21e-cff40a35c1b0 · outbound

This paper cites Molecular ge nerative graph neural networks for drug discovery ,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning Molecular ge nerative graph neural networks for drug discovery ,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:42:08.967992Z

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Observation 8a657452-920f-4b58-a1c8-70d7f48afdc1 · outbound

This paper cites Estimated resear ch and development investment needed to bring a new medicine to market, 2009-2018,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning Estimated resear ch and development investment needed to bring a new medicine to market, 2009-2018,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:42:08.959827Z

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This paper cites A crowdsourcing deve l- opment approach based on a neuro-fuzzy network for creating innovative product concepts,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning A crowdsourcing deve l- opment approach based on a neuro-fuzzy network for creating innovative product concepts,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:42:08.950794Z

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Observation beb30582-1f0b-4e88-beff-f5651fb66288 · outbound

This paper cites Discovery of nov el inhibitors of a critical brain enzyme using a homology model and a deep convolutional neural network,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning Discovery of nov el inhibitors of a critical brain enzyme using a homology model and a deep convolutional neural network,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:42:08.941588Z

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Observation 05e5d1dd-da52-410d-be8d-c59786c678df · outbound

This paper cites The light and dark sides of virtual scree ning: what is there to know?.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning The light and dark sides of virtual scree ning: what is there to know?

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:42:08.932040Z

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Observation 4fc75daa-7b14-4df5-bacf-9aa20d7b21b1 · outbound

This paper cites Machine lear ning in virtual screening,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning Machine lear ning in virtual screening,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:42:08.922368Z

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Observation e0535aec-fa76-4744-86f1-fcc49b1f1bd6 · outbound

This paper cites Automated de novo drug desi gn: are we nearly there yet?.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning Automated de novo drug desi gn: are we nearly there yet?

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:42:08.913148Z

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Observation 53a3063c-55f8-415c-83a3-7e7a264ea40e · outbound

This paper cites A review on applications of com puta- tional methods in drug screening and design,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning A review on applications of com puta- tional methods in drug screening and design,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:42:08.904161Z

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Observation fab9dacb-fd6a-4489-8279-21cf7e612025 · outbound

This paper cites Discovery and structure–activity analysis of selective e strogen receptor modulators via similarity-based virtual screeni ng,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning Discovery and structure–activity analysis of selective e strogen receptor modulators via similarity-based virtual screeni ng,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:42:08.895232Z

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Observation 7a4e261d-00d7-4f35-9c34-e230733e575b · outbound

This paper cites Mol ec- ular graph enhanced transformer for retrosynthesis predic tion,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning Mol ec- ular graph enhanced transformer for retrosynthesis predic tion,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:42:08.885804Z

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Observation ce27d3b6-9f1e-4fc6-9487-f87d5c35ca4c · outbound

This paper cites Bifunctional tools to study adenosine receptors ,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning Bifunctional tools to study adenosine receptors ,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:42:08.877243Z

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Observation 68b30d03-d714-467c-9891-b36b0632ac9a · outbound

This paper cites Molecula r property prediction and molecular design using a supervise d grammar variational autoencoder,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning Molecula r property prediction and molecular design using a supervise d grammar variational autoencoder,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:42:08.869533Z

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Observation 95205c9f-85a3-4630-9507-4f131c9dfaed · outbound

This paper cites Atte ntion- based generative models for de novo molecular design,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning Atte ntion- based generative models for de novo molecular design,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:42:08.861808Z

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Observation fd1e3c7f-012b-4576-b89f-b866797c68ba · outbound

This paper cites MolGAN: An implicit generative model for small molecular graphs.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning MolGAN: An implicit generative model for small molecular graphs

Reference 17

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Observation bdc78d03-2fcd-4aa2-9982-0e0d672e5f6b · outbound

This paper cites Transf ormer- based objective-reinforced generative adversarial netwo rk to gen- erate desired molecules,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning Transf ormer- based objective-reinforced generative adversarial netwo rk to gen- erate desired molecules,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:42:08.853439Z

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Observation 1da811b0-14b2-456d-aa9d-669c2af4c39d · outbound

This paper cites SpotGAN: A reverse-transformer GAN generates scaffold-constrained molecules with property o ptimiza- tion,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning SpotGAN: A reverse-transformer GAN generates scaffold-constrained molecules with property o ptimiza- tion,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:42:08.845140Z

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Observation de3e162a-d05b-403b-9026-b662a80e271d · outbound

This paper cites The impact of assay technology as applied to safety assessment in reducing compound attritio n in drug discovery ,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning The impact of assay technology as applied to safety assessment in reducing compound attritio n in drug discovery ,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:42:08.836171Z

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Observation 70da2411-a130-43ec-a2da-e5a2152af4b1 · outbound

This paper cites De novo structure-based drug design using deep learning,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning De novo structure-based drug design using deep learning,

Reference 21

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Observation e13fb895-7bd0-4261-a1f1-a422ff61ae78 · outbound

This paper cites An in silico explaina ble multiparameter optimization approach for de novo drug desi gn against proteins from the central nervous system,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning An in silico explaina ble multiparameter optimization approach for de novo drug desi gn against proteins from the central nervous system,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:42:08.816420Z

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Observation 34abf542-c960-4392-94d7-d32a747df34f · outbound

This paper cites De novo generation of hit-like molecules from g ene expression signatures using artiﬁcial intelligence,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning De novo generation of hit-like molecules from g ene expression signatures using artiﬁcial intelligence,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:42:08.806225Z

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Observation 2f40462f-8bae-43ac-b1a5-3e52c409f52a · outbound

This paper cites TRIOMPHE: Transcriptome- based inference and generation of molecules with desired phenoty pes by machine learning,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning TRIOMPHE: Transcriptome- based inference and generation of molecules with desired phenoty pes by machine learning,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:42:08.797451Z

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Observation 363588cd-1cc6-4156-af46-57ff87656ec9 · outbound

This paper cites Captur ing temporal dynamics of users’ preferences from purchase hist ory big data for recommendation system,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning Captur ing temporal dynamics of users’ preferences from purchase hist ory big data for recommendation system,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:42:08.788138Z

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Observation d84c2ea0-ea5e-465a-946f-8813e240489e · outbound

This paper cites A multi-factor approa ch for stock price prediction by using recurrent neural networ ks,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning A multi-factor approa ch for stock price prediction by using recurrent neural networ ks,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:42:08.778011Z

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This paper cites Time series forecasting of petro leum production using deep LSTM recurrent networks,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning Time series forecasting of petro leum production using deep LSTM recurrent networks,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:42:08.767913Z

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Observation 00eeac7d-fa08-4859-9285-2bc7f9666863 · outbound

This paper cites From theory to experiment: transformer-based generation enables rapid discovery of novel reactions,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning From theory to experiment: transformer-based generation enables rapid discovery of novel reactions,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:42:08.759097Z

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Observation 70f06166-b1ce-44cf-8985-70a5d6532e66 · outbound

This paper cites an unresolved cited work.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning Unresolved cited work

Reference 29

Resolution: unresolved; raw_fallback, observed 2026-08-11T00:42:08.750939Z

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Observation efc9ed44-7d5b-4894-8cd5-1cde0c84ed78 · outbound

This paper cites Structure-based design , synthesis, and evaluation of peptide-mimetic SARS 3CL prote ase inhibitors,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning Structure-based design , synthesis, and evaluation of peptide-mimetic SARS 3CL prote ase inhibitors,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:42:08.743051Z

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Observation a304b7bb-81db-47ed-a0f8-5bc3fd803458 · outbound

This paper cites Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:42:08.734367Z

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Observation 65a1b783-0040-4d76-aa93-7d391f9f4697 · outbound

This paper cites Junction tree var iational au- toencoder for molecular graph generation,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning Junction tree var iational au- toencoder for molecular graph generation,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:42:08.725962Z

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Observation 13e2b0c0-04e8-4ab6-bae2-92d94a715799 · outbound

This paper cites Interpretable molec ular graph generation via monotonic constraints,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning Interpretable molec ular graph generation via monotonic constraints,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:42:08.716664Z

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Observation 3ba1c813-2528-459f-ba24-cd4cf364f6ca · outbound

This paper cites Small molecul e generation via disentangled representation learning,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning Small molecul e generation via disentangled representation learning,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:42:08.706808Z

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Observation ffa002bf-811b-4dda-bcb3-90c6b9bce864 · outbound

This paper cites MoFlow: an invertible ﬂow model for gen- erating molecular graphs,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning MoFlow: an invertible ﬂow model for gen- erating molecular graphs,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:42:08.697369Z

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Observation 43ee6369-82c9-497f-b32d-749619f65df8 · outbound

This paper cites FastFlows: Flow-Based Models for Molecular Graph Generation.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning FastFlows: Flow-Based Models for Molecular Graph Generation

Reference 36

Resolution: verified exact; local_arxiv, observed 2026-08-11T00:42:08.431153Z

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Observation 275d69ac-83c8-4a0d-95c5-82c604b78511 · outbound

This paper cites DiGress: Discrete denoising diffusion for gr aph gen- eration,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning DiGress: Discrete denoising diffusion for gr aph gen- eration,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:42:08.687873Z

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Observation d618104c-76b4-4922-b811-f025b90859c4 · outbound

This paper cites Continuous control with deep reinforcement learning.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning Continuous control with deep reinforcement learning

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-11T00:42:08.286068Z

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Observation affc9c7e-39c0-44af-8747-5c6583d44cd1 · outbound

This paper cites Adversarial learned mol ecular graph inference and generation,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning Adversarial learned mol ecular graph inference and generation,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:42:08.678548Z

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Observation 33f768ab-ce26-4e8c-b6e4-7fb59f067443 · outbound

This paper cites Gr ammar variational autoencoder,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning Gr ammar variational autoencoder,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:42:08.669355Z

Source-reported events for the cited work

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Observation 23bcd16e-3f2f-44fe-a382-ce8901277ce4 · outbound

This paper cites DNMG: Deep molecular generative model by fusion of 3d information for de novo drug design,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning DNMG: Deep molecular generative model by fusion of 3d information for de novo drug design,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:42:08.659456Z

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 2c23567f-8bcb-43b0-9c05-b706fa5f623a · outbound

This paper cites Monte-carlo simulation balan cing,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning Monte-carlo simulation balan cing,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:42:08.650006Z

Source-reported events for the cited work

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Observation 92868a58-4478-4e11-a3c1-5885bdb0141f · outbound

This paper cites A review of molecular representation in the age of machine learning,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning A review of molecular representation in the age of machine learning,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:42:08.640504Z

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Observation 064d4485-bcd6-4f6d-a5cf-d5c9fe4a5eac · outbound

This paper cites SELF- IES and the future of molecular string representations,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning SELF- IES and the future of molecular string representations,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:42:08.631403Z

Source-reported events for the cited work

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Observation eb00b092-eb18-4e19-890a-0cfd1c1f721c · outbound

This paper cites Hierarchical Structure Enhances the Convergence and Generalizability of Linear Molecular Representation.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning Hierarchical Structure Enhances the Convergence and Generalizability of Linear Molecular Representation

Reference 45

Resolution: verified exact; local_arxiv, observed 2026-08-11T00:42:08.409293Z

Source-reported events for the cited work

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Observation f58b1d11-5677-49e1-8f13-bb9462d8cd7d · outbound

This paper cites Predicting physiolo gical effects of chemical substances using natural language proc essing,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning Predicting physiolo gical effects of chemical substances using natural language proc essing,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:42:08.622741Z

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 3db83c86-ac8f-45fc-907f-1e9299470cf9 · outbound

This paper cites Advances in machine lea rn- ing with chemical language models in molecular property and reaction outcome predictions,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning Advances in machine lea rn- ing with chemical language models in molecular property and reaction outcome predictions,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:42:08.613541Z

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 005125c1-e8f4-4abf-8b5d-70d68f296813 · outbound

This paper cites Exploring chemical space — generative models and their evaluation,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning Exploring chemical space — generative models and their evaluation,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:42:08.604479Z

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 99f046f9-0903-4b9b-9291-7c39ed723c5c · outbound

This paper cites Lifelong genera- tive modeling,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning Lifelong genera- tive modeling,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:42:08.596114Z

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 f7e366f6-dc46-4e68-b5f3-6bc844a64344 · outbound

This paper cites Kullback-leibler divergence,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning Kullback-leibler divergence,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:42:08.587143Z

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 02630eb3-3b90-49d4-aaef-ab70c37e5be4 · outbound

This paper cites LINCS canvas browser: interactive web app to query , browse and interrogate lincs l1000 gene expression signatures,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning LINCS canvas browser: interactive web app to query , browse and interrogate lincs l1000 gene expression signatures,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:42:08.577326Z

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 a8b8dfb7-34eb-496b-9bf0-5f3bef67c991 · outbound

This paper cites Extraction and analysis of signatures from the gene expression omnibus by the crowd,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning Extraction and analysis of signatures from the gene expression omnibus by the crowd,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:42:08.567443Z

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 7385a13e-e9fd-4939-8440-21dafc6012ba · outbound

This paper cites Dropout: a simple way to prevent neural networks from overﬁtting,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning Dropout: a simple way to prevent neural networks from overﬁtting,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:42:08.556575Z

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 61f0ebd4-2fca-481a-8ce1-2ae82443a191 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning Adam: A Method for Stochastic Optimization

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-11T00:42:08.330758Z

Source-reported events for the cited work

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Observation 343a9b34-3838-482c-a067-f0b625b63fff · outbound

This paper cites Quantifying the chemical beauty of drugs,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning Quantifying the chemical beauty of drugs,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:42:08.546923Z

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 27593e0d-2b3a-4beb-97da-4e52625bfbdc · outbound

This paper cites Estimation of synthetic a ccessibility score of drug-like molecules based on molecular complexity and fragment contributions,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning Estimation of synthetic a ccessibility score of drug-like molecules based on molecular complexity and fragment contributions,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:42:08.536933Z

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 dd42d43d-e3c0-44dc-be29-0e01ed791090 · outbound

This paper cites Life beyond the tanimoto co- efﬁcient: similarity measures for interaction ﬁngerprint s,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning Life beyond the tanimoto co- efﬁcient: similarity measures for interaction ﬁngerprint s,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:42:08.527074Z

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 f4c6b241-2c84-4b41-af3f-454383a910aa · outbound

This paper cites Rdkit documentation,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning Rdkit documentation,

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-11T00:42:08.341883Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 25399461-984b-4d08-8b93-83b86185f2bb · outbound

This paper cites MolFinder: an evolutionary algorit hm for the global optimization of molecular properties and the ext ensive exploration of chemical space using smiles,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning MolFinder: an evolutionary algorit hm for the global optimization of molecular properties and the ext ensive exploration of chemical space using smiles,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:42:08.511678Z

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 00a7ea57-eaed-47cc-9f4d-8519aba53b15 · outbound

This paper cites Prediction of drug-likeness using grap h convolutional attention network,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning Prediction of drug-likeness using grap h convolutional attention network,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:42:08.503029Z

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 f34c1851-e84b-462f-8d59-2a6e16f819ba · outbound

This paper cites Extended-connectivity ﬁngerpr ints,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning Extended-connectivity ﬁngerpr ints,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:42:08.493608Z

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 3c8a5a70-e5c1-4bf9-a069-766a6816462a · outbound

This paper cites Improving MRI segmentation with probabilistic GHSOM and multiobjective optimization,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning Improving MRI segmentation with probabilistic GHSOM and multiobjective optimization,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:42:08.483380Z

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 7ab8a1ad-3267-4c91-8e74-17a63697f984 · outbound

This paper cites Single-layer artiﬁcial neural networks for gene expressio n analy- sis,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning Single-layer artiﬁcial neural networks for gene expressio n analy- sis,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:42:08.473243Z

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 ad5c7505-3bd0-44eb-8aa0-1e204912ba81 · outbound

This paper cites De novo drug design based on patient gene expression proﬁles vi a deep learning,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning De novo drug design based on patient gene expression proﬁles vi a deep learning,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:42:08.462431Z

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 f7ac7696-4d26-490d-a1a0-543e29709017 · outbound

This paper cites Prediction of cancer drugs by chemical-chem ical interactions,.

De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning Prediction of cancer drugs by chemical-chem ical interactions,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:42:08.452080Z

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.

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