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Found 11 papers, 5 papers with code
Strategies for Pre-training Graph Neural Networks
Many applications of machine learning require a model to make accurate pre-dictions on test examples that are distributionally different from training ones, while task-specific labels are scarce during training.
Ranked #3 on
Molecular Property Prediction
on ToxCast
Weakly-Supervised Deep Learning of Heat Transport via Physics Informed Loss
In typical machine learning tasks and applications, it is necessary to obtain or create large labeled datasets in order to to achieve high performance.
Improved Training with Curriculum GANs
We demonstrate that this strategy is key to obtaining state-of-the-art results in image generation.
Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation
Generating novel graph structures that optimize given objectives while obeying some given underlying rules is fundamental for chemistry, biology and social science research.
Retrosynthetic reaction prediction using neural sequence-to-sequence models
We describe a fully data driven model that learns to perform a retrosynthetic reaction prediction task, which is treated as a sequence-to-sequence mapping problem.
MoleculeNet: A Benchmark for Molecular Machine Learning
However, algorithmic progress has been limited due to the lack of a standard benchmark to compare the efficacy of proposed methods; most new algorithms are benchmarked on different datasets making it challenging to gauge the quality of proposed methods.
Low Data Drug Discovery with One-shot Learning
Recent advances in machine learning have made significant contributions to drug discovery.
Ranked #2 on
Molecular Property Prediction
on Tox21
Modeling Industrial ADMET Data with Multitask Networks
Deep learning methods such as multitask neural networks have recently been applied to ligand-based virtual screening and other drug discovery applications.
ROCS-Derived Features for Virtual Screening
Rapid overlay of chemical structures (ROCS) is a standard tool for the calculation of 3D shape and chemical ("color") similarity.
Molecular Graph Convolutions: Moving Beyond Fingerprints
Molecular "fingerprints" encoding structural information are the workhorse of cheminformatics and machine learning in drug discovery applications.
Ranked #4 on
Graph Regression
on Lipophilicity
Massively Multitask Networks for Drug Discovery
Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources.
