Research graph
References from Generative models for molecule generation. Local targets link to admitted publications; unresolved targets remain external evidence.
Principal component analysis
10.1002/wics.101 · 2010 · External reference
Importance of data curation in QSAR studies especially while modeling large-size datasets
10.1007/978-1-0716-0150-1_5 · 2020 · External reference
SMILES-based deep generative scaffold decorator for de-novo drug design
10.1186/s13321-020-00441-8 · 2020 · External reference
Generative models for molecular discovery: recent advances and challenges
10.1002/wcms.1608 · 2022 · External reference
Using GANs with adaptive training data to search for new molecules
10.1186/s13321-021-00494-3 · 2021 · External reference
Deep generative modelling: a comparative review of VAEs, GANs, normalizing flows, energy-based and autoregressive models
10.1109/tpami.2021.3116668 · 2022 · External reference
GuacaMol: benchmarking models for de novo molecular design
10.1021/acs.jcim.8b00839 · 2019 · External reference
Effect of lipophilicity on drug distribution and elimination: influence of obesity
10.1111/bcp.14735 · 2021 · External reference
Metrics for deep generative models. In International Conference on Artificial Intelligence and Statistics
2018 · External reference
Unresolved reference
External reference
Recent Advances in QSAR Studies
2010 · External reference
3-D inorganic crystal structure generation and property prediction via representation learning
10.1021/acs.jcim.0c00464 · 2020 · External reference
Syntax-directed variational autoencoder for structured data
2018 · External reference
Hyperspherical variational auto-encoders 2
2018 · External reference
Prediction reliability of QSAR models: an overview of various validation tools
10.1007/s00204-022-03252-y · 2022 · External reference
Greener chemicals for the future: QSAR modelling of the PBT index using ETA descriptors
10.1080/1062936x.2018.1436086 · 2018 · External reference
Unresolved reference
External reference
Lipophilicity and hydrophobicity considerations in bio-enabling oral formulations approaches—a PEARRL review
10.1111/jphp.12984 · 2019 · External reference
Zero-VAE-GAN: generating unseen features for generalized and transductive zero-shot learning
10.1109/tip.2020.2964429 · 2020 · External reference
Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules
2019 · External reference
Quantitative structure–activity relationship prediction of blood-to-brain partitioning behavior using support vector machine
10.1016/j.ejps.2012.06.021 · 2012 · External reference
Automatic chemical design using a data-driven continuous representation of molecules
10.1021/acscentsci.7b00572 · 2018 · External reference
Feature selection methods in QSAR studies
10.5740/jaoacint.sge_goodarzi · 2012 · External reference
Generative adversarial networks
10.1145/3422622 · 2014 · External reference
Unresolved reference
External reference
Correlation of biological activity of phenoxyacetic acids with Hammett substituent constants and partition coefficients
10.1038/194178b0 · 1962 · External reference
A baseline for detecting misclassified and out-of-distribution examples in neural networks
2017 · External reference
Beta-vae: learning basic visual concepts with a constrained variational framework
2017 · External reference
Long short-term memory
10.1162/neco.1997.9.8.1735 · 1997 · External reference
Unresolved reference
External reference
10.24963/ijcai.2017/273
10.24963/ijcai.2017/273 · External reference
Junction tree variational autoencoder for molecular graph generation
2018 · External reference
Hierarchical generation of molecular graphs using structural motifs
2020 · External reference
The relativistic discriminator: a key element missing from standard GAN
2018 · External reference
Computer aided design of experiments
10.1080/00401706.1969.10490666 · 1969 · External reference
Embedding of molecular structure using molecular hypergraph variational autoencoder with metric learning
10.1002/minf.202000203 · 2021 · External reference
Robust cross-validation of linear regression QSAR models
10.1021/ci800209k · 2008 · External reference
Self-referencing embedded strings (SELFIES): a 100% robust molecular string representation
10.1088/2632-2153/aba947 · 2020 · External reference
Grammar variational autoencoder
2017 · External reference
Compressed graph representation for scalable molecular graph generation
10.1186/s13321-020-00463-2 · 2020 · External reference
A brief introduction to generative models
2021 · External reference
Autoencoding beyond pixels using a learned similarity metric
2016 · External reference
Human nephrotoxicity prediction models for three types of kidney injury based on data sets of pharmacological compounds and their metabolites
10.1021/tx400249t · 2013 · External reference
The global k-means clustering algorithm
10.1016/s0031-3203(02)00060-2 · 2003 · External reference
Use of machine learning approaches for novel drug discovery
10.1517/17460441.2016.1146250 · 2016 · External reference
Constrained graph variational autoencoders for molecule design
2018 · External reference
Machine learning based QSAR for discovering potential drug candidate from endemic plants of Sri Lanka- case study: HIV-1 RT
2010 · External reference
Constrained generation of semantically valid graphs via regularizing variational autoencoders
2018 · External reference
Masked graph modeling for molecule generation
10.1038/s41467-021-23415-2 · 2021 · External reference
Comprehensive strategies of machine-learning-based quantitative structure-activity relationship models
10.1016/j.isci.2021.103052 · 2021 · External reference
Penalized variational autoencoder for molecular design
2019 · External reference
Unresolved reference
External reference
Future perspective: harnessing the power of artificial intelligence in the generation of new peptide drugs
10.3390/biom14101303 · 2024 · External reference
Learning latent space energy-based prior model
2020 · External reference
Descriptor-based profile analysis of kinase inhibitors to predict inhibitory activity and to grasp kinase selectivity
10.5012/bkcs.2013.34.9.2680 · 2013 · External reference
Quantitative structure-activity relationship (QSAR): modeling approaches to biological applications
2019 · External reference
Integrating high-performance computing, machine learning, data management workflows, and infrastructures for multiscale simulations and nanomaterials technologies
10.3762/bjnano.15.119 · 2024 · External reference
Estimation of the size of drug-like chemical space based on GDB-17 data
10.1007/s10822-013-9672-4 · 2013 · External reference
Molecular sets (MOSES): a benchmarking platform for molecular generation models
10.3389/fphar.2020.565644 · 2020 · External reference
Unresolved reference
External reference
Exploring chemical space for drug discovery using the chemical universe database
10.1021/cn3000422 · 2012 · External reference
Molecular dynamics fingerprints (MDFP): machine learning from MD data to predict free-energy differences
10.1021/acs.jcim.6b00778 · 2017 · External reference
Quantitative structure-activity relationships (QSARs): a few validation methods and software tools developed at the DTC laboratory
2018 · External reference
On a simple approach for determining applicability domain of QSAR models
10.1016/j.chemolab.2015.04.013 · 2015 · External reference
Statistical methods in QSAR/QSPR
2015 · External reference
NEVAE: a deep generative model for molecular graphs∗
2020 · External reference
VAE-Sim: a novel molecular similarity measure based on a variational autoencoder
10.3390/molecules25153446 · 2020 · External reference
Variational autoencoders - theory and applications: exploring variational autoencoder models and their applications in generative modeling, representation learning, and beyond
2024 · External reference
Characterizing the latent space of molecular deep generative models with persistent homology metrics
2020 · External reference
Generative models for automatic chemical design
2020 · External reference
Unresolved reference
External reference
Use of metal/metal oxide spherical cluster and hydroxyl metal coordination complex for descriptor calculation in development of nanoparticle cytotoxicity classification model
10.1080/1062936x.2017.1400998 · 2017 · External reference
Meta-analysis of Daphnia magna nanotoxicity experiments in accordance with test guidelines
10.1039/c7en01127j · 2018 · External reference
Reinforcement learning for molecular design guided by quantum mechanics
2020 · External reference
Graphvae: towards generation of small graphs using variational autoencoders
2018 · External reference
Integrated machine learning, molecular docking and 3D-QSAR based approach for identification of potential inhibitors of trypanosomal N-myristoyltransferase
10.1039/c6mb00574h · 2016 · External reference
Artificial intelligence powered Metaverse: analysis, challenges and future perspectives
10.1007/s10462-023-10641-x · 2024 · External reference
Unresolved reference
External reference
Drug targeting
10.1016/s0928-0987(00)00166-4 · 2000 · External reference
Recent advances and application of generative adversarial networks in drug discovery, development, and targeting
10.1016/j.ailsci.2022.100045 · 2022 · External reference
Neural discrete representation learning
2017 · External reference
Recent advances in variational autoencoders with representation learning for biomedical informatics: a survey
10.1109/access.2020.3048309 · 2020 · External reference
SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules
10.1021/ci00057a005 · 1988 · External reference
PLS-regression: a basic tool of chemometrics
10.1016/s0169-7439(01)00155-1 · 2001 · External reference
Machine learning based toxicity prediction: from chemical structural description to transcriptome analysis
10.3390/ijms19082358 · 2018 · External reference
F-VAEGAN-D2: a feature generating framework for any-shot learning
10.1109/cvpr.2019.01052 · 2019 · External reference
Learning neural generative dynamics for molecular conformation generation
2021 · External reference
Re-balancing variational autoencoder loss for molecule sequence generation
2020 · External reference
Comparative study of deep generative models on chemical space coverage
10.1021/acs.jcim.0c01328 · 2021 · External reference
GANsDTA: predicting drug-target binding affinity using GANs
10.3389/fgene.2019.01243 · 2020 · External reference
Unresolved reference
External reference
S3VAE: self-supervised sequential VAE for representation disentanglement and data generation
2020 · External reference
Embedding of molecular structure using molecular hypergraph variational autoencoder with metric learning
10.1002/minf.202000203 · ExternalCitation · doi-reference
Generative models for molecular discovery: recent advances and challenges
10.1002/wcms.1608 · ExternalCitation · doi-reference
Principal component analysis
10.1002/wics.101 · ExternalCitation · doi-reference
Importance of data curation in QSAR studies especially while modeling large-size datasets
10.1007/978-1-0716-0150-1_5 · ExternalCitation · doi-reference
Prediction reliability of QSAR models: an overview of various validation tools
10.1007/s00204-022-03252-y · ExternalCitation · doi-reference
Artificial intelligence powered Metaverse: analysis, challenges and future perspectives
10.1007/s10462-023-10641-x · ExternalCitation · doi-reference
Estimation of the size of drug-like chemical space based on GDB-17 data
10.1007/s10822-013-9672-4 · ExternalCitation · doi-reference
Recent advances and application of generative adversarial networks in drug discovery, development, and targeting
10.1016/j.ailsci.2022.100045 · ExternalCitation · doi-reference
On a simple approach for determining applicability domain of QSAR models
10.1016/j.chemolab.2015.04.013 · ExternalCitation · doi-reference
Quantitative structure–activity relationship prediction of blood-to-brain partitioning behavior using support vector machine
10.1016/j.ejps.2012.06.021 · ExternalCitation · doi-reference
Comprehensive strategies of machine-learning-based quantitative structure-activity relationship models
10.1016/j.isci.2021.103052 · ExternalCitation · doi-reference
The global k-means clustering algorithm
10.1016/s0031-3203(02)00060-2 · ExternalCitation · doi-reference
PLS-regression: a basic tool of chemometrics
10.1016/s0169-7439(01)00155-1 · ExternalCitation · doi-reference
Drug targeting
10.1016/s0928-0987(00)00166-4 · ExternalCitation · doi-reference
3-D inorganic crystal structure generation and property prediction via representation learning
10.1021/acs.jcim.0c00464 · ExternalCitation · doi-reference
Comparative study of deep generative models on chemical space coverage
10.1021/acs.jcim.0c01328 · ExternalCitation · doi-reference
Molecular dynamics fingerprints (MDFP): machine learning from MD data to predict free-energy differences
10.1021/acs.jcim.6b00778 · ExternalCitation · doi-reference
GuacaMol: benchmarking models for de novo molecular design
10.1021/acs.jcim.8b00839 · ExternalCitation · doi-reference
Automatic chemical design using a data-driven continuous representation of molecules
10.1021/acscentsci.7b00572 · ExternalCitation · doi-reference
SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules
10.1021/ci00057a005 · ExternalCitation · doi-reference
Robust cross-validation of linear regression QSAR models
10.1021/ci800209k · ExternalCitation · doi-reference
Exploring chemical space for drug discovery using the chemical universe database
10.1021/cn3000422 · ExternalCitation · doi-reference
Human nephrotoxicity prediction models for three types of kidney injury based on data sets of pharmacological compounds and their metabolites
10.1021/tx400249t · ExternalCitation · doi-reference
Correlation of biological activity of phenoxyacetic acids with Hammett substituent constants and partition coefficients
10.1038/194178b0 · ExternalCitation · doi-reference
Masked graph modeling for molecule generation
10.1038/s41467-021-23415-2 · ExternalCitation · doi-reference
Integrated machine learning, molecular docking and 3D-QSAR based approach for identification of potential inhibitors of trypanosomal N-myristoyltransferase
10.1039/c6mb00574h · ExternalCitation · doi-reference
Meta-analysis of Daphnia magna nanotoxicity experiments in accordance with test guidelines
10.1039/c7en01127j · ExternalCitation · doi-reference
Computer aided design of experiments
10.1080/00401706.1969.10490666 · ExternalCitation · doi-reference
Use of metal/metal oxide spherical cluster and hydroxyl metal coordination complex for descriptor calculation in development of nanoparticle cytotoxicity classification model
10.1080/1062936x.2017.1400998 · ExternalCitation · doi-reference
Greener chemicals for the future: QSAR modelling of the PBT index using ETA descriptors
10.1080/1062936x.2018.1436086 · ExternalCitation · doi-reference
Self-referencing embedded strings (SELFIES): a 100% robust molecular string representation
10.1088/2632-2153/aba947 · ExternalCitation · doi-reference
Recent advances in variational autoencoders with representation learning for biomedical informatics: a survey
10.1109/access.2020.3048309 · ExternalCitation · doi-reference
F-VAEGAN-D2: a feature generating framework for any-shot learning
10.1109/cvpr.2019.01052 · ExternalCitation · doi-reference
Zero-VAE-GAN: generating unseen features for generalized and transductive zero-shot learning
10.1109/tip.2020.2964429 · ExternalCitation · doi-reference
Deep generative modelling: a comparative review of VAEs, GANs, normalizing flows, energy-based and autoregressive models
10.1109/tpami.2021.3116668 · ExternalCitation · doi-reference
Effect of lipophilicity on drug distribution and elimination: influence of obesity
10.1111/bcp.14735 · ExternalCitation · doi-reference
Lipophilicity and hydrophobicity considerations in bio-enabling oral formulations approaches—a PEARRL review
10.1111/jphp.12984 · ExternalCitation · doi-reference
Generative adversarial networks
10.1145/3422622 · ExternalCitation · doi-reference
Long short-term memory
10.1162/neco.1997.9.8.1735 · ExternalCitation · doi-reference
SMILES-based deep generative scaffold decorator for de-novo drug design
10.1186/s13321-020-00441-8 · ExternalCitation · doi-reference
Compressed graph representation for scalable molecular graph generation
10.1186/s13321-020-00463-2 · ExternalCitation · doi-reference
Using GANs with adaptive training data to search for new molecules
10.1186/s13321-021-00494-3 · ExternalCitation · doi-reference
Use of machine learning approaches for novel drug discovery
10.1517/17460441.2016.1146250 · ExternalCitation · doi-reference
10.24963/ijcai.2017/273
10.24963/ijcai.2017/273 · ExternalCitation · doi-reference
GANsDTA: predicting drug-target binding affinity using GANs
10.3389/fgene.2019.01243 · ExternalCitation · doi-reference
Molecular sets (MOSES): a benchmarking platform for molecular generation models
10.3389/fphar.2020.565644 · ExternalCitation · doi-reference
Future perspective: harnessing the power of artificial intelligence in the generation of new peptide drugs
10.3390/biom14101303 · ExternalCitation · doi-reference
Machine learning based toxicity prediction: from chemical structural description to transcriptome analysis
10.3390/ijms19082358 · ExternalCitation · doi-reference
VAE-Sim: a novel molecular similarity measure based on a variational autoencoder
10.3390/molecules25153446 · ExternalCitation · doi-reference
Integrating high-performance computing, machine learning, data management workflows, and infrastructures for multiscale simulations and nanomaterials technologies
10.3762/bjnano.15.119 · ExternalCitation · doi-reference
Descriptor-based profile analysis of kinase inhibitors to predict inhibitory activity and to grasp kinase selectivity
10.5012/bkcs.2013.34.9.2680 · ExternalCitation · doi-reference
Feature selection methods in QSAR studies
10.5740/jaoacint.sge_goodarzi · ExternalCitation · doi-reference
