A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design | Paper Citation Record

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This paper cites Innovation in the pharmaceutical industry: New estimates of r&d costs,.

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design Innovation in the pharmaceutical industry: New estimates of r&d costs,

Reference 1

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This paper cites An analysis of the attrition of drug candidates from four major pharmaceutical companies,.

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design An analysis of the attrition of drug candidates from four major pharmaceutical companies,

Reference 2

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This paper cites 2015 fda drug approvals,.

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design 2015 fda drug approvals,

Reference 3

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This paper cites Estimation of the size of drug-like chemical space based on gdb -17 data,.

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design Estimation of the size of drug-like chemical space based on gdb -17 data,

Reference 4

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This paper cites Rethinking drug design in the artificial intelligence era,.

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design Rethinking drug design in the artificial intelligence era,

Reference 5

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This paper cites Applications of machine learning in drug discovery and development,.

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design Applications of machine learning in drug discovery and development,

Reference 6

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This paper cites The rise of deep learning in drug discovery,.

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design The rise of deep learning in drug discovery,

Reference 7

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This paper cites Deep learning for molecular design —a review of the state of the art,.

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design Deep learning for molecular design —a review of the state of the art,

Reference 8

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This paper cites The´ power of deep learning to ligand -based novel drug discovery,.

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design The´ power of deep learning to ligand -based novel drug discovery,

Reference 9

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This paper cites Inverse molecular design using machine learning: Generative models for matter engineering,.

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design Inverse molecular design using machine learning: Generative models for matter engineering,

Reference 10

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This paper cites Applications of deep learning in molecule generation and molecular property prediction,.

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design Applications of deep learning in molecule generation and molecular property prediction,

Reference 11

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This paper cites A deep learning approach to antibiotic discovery,.

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design A deep learning approach to antibiotic discovery,

Reference 12

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This paper cites Deep learning enables rapid identification of potent ddr1 kinase inhibitors,.

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design Deep learning enables rapid identification of potent ddr1 kinase inhibitors,

Reference 13

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This paper cites Guacamol: Benchmarking models for de novo molecular design,.

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design Guacamol: Benchmarking models for de novo molecular design,

Reference 14

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This paper cites De novo molecular design and generative models,.

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design De novo molecular design and generative models,

Reference 15

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This paper cites Molecular Sets (MOSES): A B enchmarking Platform for Molecular Generation Models,.

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design Molecular Sets (MOSES): A B enchmarking Platform for Molecular Generation Models,

Reference 16

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This paper cites A practical overview of quantitative structure-activity relationship,.

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design A practical overview of quantitative structure-activity relationship,

Reference 17

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This paper cites Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules,.

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules,

Reference 18

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This paper cites Convolutional networks on graphs for learning molecular fingerprints,.

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design Convolutional networks on graphs for learning molecular fingerprints,

Reference 19

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This paper cites Extended-connectivity fingerprints,.

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design Extended-connectivity fingerprints,

Reference 20

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This paper cites 3d molecular representations based on the wave transform for convolutional neural networks,.

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design 3d molecular representations based on the wave transform for convolutional neural networks,

Reference 21

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This paper cites Generating focused molecule libraries for drug discovery with recurrent neural networks,.

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design Generating focused molecule libraries for drug discovery with recurrent neural networks,

Reference 22

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This paper cites Generative recurrent networks for de novo drug design,.

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design Generative recurrent networks for de novo drug design,

Reference 23

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This paper cites Automatic chemical design using a data-driven continuous representation of molecules,.

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design Automatic chemical design using a data-driven continuous representation of molecules,

Reference 24

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This paper cites Auto-Encoding Variational Bayes.

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design Auto-Encoding Variational Bayes

Reference 25

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This paper cites De novo design of new chemical entities with reinvent,.

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design De novo design of new chemical entities with reinvent,

Reference 26

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This paper cites A de novo molecular generation method using latent vector based generative adversarial network,.

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design A de novo molecular generation method using latent vector based generative adversarial network,

Reference 27

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This paper cites Generative adversarial nets,.

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design Generative adversarial nets,

Reference 28

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This paper cites GraphNVP: An Invertible Flow Model for Generating Molecular Graphs.

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design GraphNVP: An Invertible Flow Model for Generating Molecular Graphs

Reference 29

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This paper cites Mermaid: Metaphors for molecular generation,.

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design Mermaid: Metaphors for molecular generation,

Reference 30

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This paper cites Molecular denovo design through deep reinforcement learning,.

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design Molecular denovo design through deep reinforcement learning,

Reference 31

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This paper cites Deep reinforcement learning for de novo drug design,.

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design Deep reinforcement learning for de novo drug design,

Reference 32

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This paper cites SMILES Transformer: Pre-trained Molecular Fingerprint for Low Data Drug Discovery.

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design SMILES Transformer: Pre-trained Molecular Fingerprint for Low Data Drug Discovery

Reference 33

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

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design Mol - cyclegan: a generative model for molecular optimization,

Reference 34

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This paper cites The molecular transformer for organic reaction prediction and symbolic inference,.

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design The molecular transformer for organic reaction prediction and symbolic inference,

Reference 35

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This paper cites Frechet chemnet distance: A metric for generative models for molecules´ in drug discovery,.

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design Frechet chemnet distance: A metric for generative models for molecules´ in drug discovery,

Reference 36

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

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design Estimation of synthetic accessibility score of drug -like molecules based on molecular complexity and fragment contributions,

Reference 37

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This paper cites Zinc 15–ligand discovery for everyone,.

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design Zinc 15–ligand discovery for everyone,

Reference 38

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This paper cites The pains problem in virtual screening,.

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design The pains problem in virtual screening,

Reference 39

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This paper cites The properties of known drugs. 1. molecular frameworks,.

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design The properties of known drugs. 1. molecular frameworks,

Reference 40

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

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design Junction tree variational autoencoder for molecular graph generation,

Reference 41

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This paper cites Prediction of physicochemical parameters by atomic contributions,.

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design Prediction of physicochemical parameters by atomic contributions,

Reference 42

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This paper cites Quantifying the chemical beauty of drugs,.

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design Quantifying the chemical beauty of drugs,

Reference 43

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This paper cites Randomized smiles strings improve the quality of molecular generative models,.

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design Randomized smiles strings improve the quality of molecular generative models,

Reference 44

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This paper cites Learning con-´ tinuous and data-driven molecular descriptors by translating equivalent chemical representations,.

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design Learning con-´ tinuous and data-driven molecular descriptors by translating equivalent chemical representations,

Reference 45

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This paper cites Application of generative autoencoder in de novo molecular design,.

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design Application of generative autoencoder in de novo molecular design,

Reference 46

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This paper cites Generative models for molecular discovery: Recent advances and challenges,.

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design Generative models for molecular discovery: Recent advances and challenges,

Reference 47

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This paper cites Machine learning force fields and coarse-grained variables in molecular dynamics: application to materials and biological systems,.

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design Machine learning force fields and coarse-grained variables in molecular dynamics: application to materials and biological systems,

Reference 48

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This paper cites Population-based de novo molecule generation, using grammatical evolution,.

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design Population-based de novo molecule generation, using grammatical evolution,

Reference 49

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This paper cites Autonomous discovery in the chemical sciences part i: Progress,.

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design Autonomous discovery in the chemical sciences part i: Progress,

Reference 50

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This paper cites Deep learning for drug design: an artificial intelligence paradigm for drug discovery in the big data era,.

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design Deep learning for drug design: an artificial intelligence paradigm for drug discovery in the big data era,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:28:42.648472Z

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This paper cites Recent applications of machine learning in drug discovery,.

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design Recent applications of machine learning in drug discovery,

Reference 52

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This paper cites A practical guide to molecular generative models,.

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design A practical guide to molecular generative models,

Reference 53

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Observation 983472b9-c477-4c63-8a87-5c8cb3ed9d2b · outbound

This paper cites Exploration strategies for discovery of interacting multi-target drugs,.

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design Exploration strategies for discovery of interacting multi-target drugs,

Reference 54

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This paper cites Artificial intelligence in drug discovery,.

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design Artificial intelligence in drug discovery,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:28:42.523342Z

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This paper cites Molgpt: Molecular generation using a transformer -decoder model,.

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design Molgpt: Molecular generation using a transformer -decoder model,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:28:42.509215Z

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This paper cites DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking.

A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking

Reference 57

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