MoleculeNet: a benchmark for molecular machine learning
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- Type
- article
- Published
- 2017-03-02
- Cited by
- 2,681
- References
- 125
- Access
- Open access
- OpenAlex
- https://openalex.org/W2594183968
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:217680306
Keywords
Benchmark (surveying), Computer science, Machine learning, Artificial intelligence, Scale (ratio)
References
- Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules
- Special Invited Paper-Additive logistic regression: A statistical view of boosting
- Neural Networks in QSAR and Drug Design
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction
- Neural networks in chemistry and drug design
- Neural networks in chemistry
- Greedy function approximation: A gradient boosting machine.
- Massively Multitask Networks for Drug Discovery
- Electronic spectra from TDDFT and machine learning in chemical space.
- The Journal of the American Chemical Society
- Maximum Unbiased Validation (MUV) Data Sets for Virtual Screening Based on PubChem Bioactivity Data
- SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules
- The relationship between Precision-Recall and ROC curves
- Acta Crystallographica Section B: Structural Science
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction
- Structural Protein–Ligand Interaction Fingerprints (SPLIF) for Structure-Based Virtual Screening: Method and Benchmark Study
- Extended-Connectivity Fingerprints
- The PDBbind database: methodologies and updates.
- NNScore 2.0: A Neural-Network Receptor–Ligand Scoring Function
- Announcing the worldwide Protein Data Bank
Cited by
- Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition
- Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules
- A Primer on Machine Learning for Materials and Its Relevance to Army Challenges
- Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity
- Opportunities and obstacles for deep learning in biology and medicine
- Intrinsic Bond Energies from a Bonds-in-Molecules Neural Network.
- Most Ligand-Based Benchmarks Measure Overfitting Rather than Accuracy
- Chemception: A Deep Neural Network with Minimal Chemistry Knowledge Matches the Performance of Expert-developed QSAR/QSPR Models
- Bond Energies from a Diatomics-in-Molecules Neural Network
- Beyond the hype: deep neural networks outperform established methods using a ChEMBL bioactivity benchmark set
- Learning Graph While Training: An Evolving Graph Convolutional Neural Network
- Seq2seq Fingerprint: An Unsupervised Deep Molecular Embedding for Drug Discovery
- Learning Graph-Level Representation for Drug Discovery
- How Much Chemistry Does a Deep Neural Network Need to Know to Make Accurate Predictions?
- Calibrated Boosting-Forest
- Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Datasets
- COMBINE: a novel drug discovery platform designed to capture insight and experience of users
- SMILES2Vec: An Interpretable General-Purpose Deep Neural Network for Predicting Chemical Properties
- ChemNet: A Transferable and Generalizable Deep Neural Network for Small-Molecule Property Prediction
- A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists
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