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Found 8 papers, 4 papers with code
Multiple-objective Reinforcement Learning for Inverse Design and Identification
The aim of the inverse chemical design is to develop new molecules with given optimized molecular properties or objectives.
IL-Net: Using Expert Knowledge to Guide the Design of Furcated Neural Networks
In this work, we develop a novel furcated neural network architecture that utilizes domain knowledge as high-level design principles of the network.
Multimodal Deep Neural Networks using Both Engineered and Learned Representations for Biodegradability Prediction
Deep learning algorithms excel at extracting patterns from raw data, and with large datasets, they have been very successful in computer vision and natural language applications.
Using Rule-Based Labels for Weak Supervised Learning: A ChemNet for Transferable Chemical Property Prediction
With access to large datasets, deep neural networks (DNN) have achieved human-level accuracy in image and speech recognition tasks.
SMILES2Vec: An Interpretable General-Purpose Deep Neural Network for Predicting Chemical Properties
Chemical databases store information in text representations, and the SMILES format is a universal standard used in many cheminformatics software.
How Much Chemistry Does a Deep Neural Network Need to Know to Make Accurate Predictions?
The meteoric rise of deep learning models in computer vision research, having achieved human-level accuracy in image recognition tasks is firm evidence of the impact of representation learning of deep neural networks.
Chemception: A Deep Neural Network with Minimal Chemistry Knowledge Matches the Performance of Expert-developed QSAR/QSPR Models
We then show how Chemception can serve as a general-purpose neural network architecture for predicting toxicity, activity, and solvation properties when trained on a modest database of 600 to 40, 000 compounds.
Deep Learning for Computational Chemistry
The rise and fall of artificial neural networks is well documented in the scientific literature of both computer science and computational chemistry.
