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Found 4 papers, 3 papers with code
Re-evaluating Retrosynthesis Algorithms with Syntheseus
The planning of how to synthesize molecules, also known as retrosynthesis, has been a growing focus of the machine learning and chemistry communities in recent years.
ChiENN: Embracing Molecular Chirality with Graph Neural Networks
Graph Neural Networks (GNNs) play a fundamental role in many deep learning problems, in particular in cheminformatics.
Step by Step Loss Goes Very Far: Multi-Step Quantization for Adversarial Text Attacks
We propose a novel gradient-based attack against transformer-based language models that searches for an adversarial example in a continuous space of token probabilities.
Relative Molecule Self-Attention Transformer
Self-supervised learning holds promise to revolutionize molecule property prediction - a central task to drug discovery and many more industries - by enabling data efficient learning from scarce experimental data.
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