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Found 8 papers, 7 papers with code
Scalable and Cost-Efficient de Novo Template-Based Molecular Generation
Template-based molecular generation offers a promising avenue for drug design by ensuring generated compounds are synthetically accessible through predefined reaction templates and building blocks.
Chimera: Accurate retrosynthesis prediction by ensembling models with diverse inductive biases
Planning and conducting chemical syntheses remains a major bottleneck in the discovery of functional small molecules, and prevents fully leveraging generative AI for molecular inverse design.
RetroGFN: Diverse and Feasible Retrosynthesis using GFlowNets
Single-step retrosynthesis aims to predict a set of reactions that lead to the creation of a target molecule, which is a crucial task in molecular discovery.
RGFN: Synthesizable Molecular Generation Using GFlowNets
Generative models hold great promise for small molecule discovery, significantly increasing the size of search space compared to traditional in silico screening libraries.
Re-evaluating Retrosynthesis Algorithms with Syntheseus
Automated Synthesis Planning has recently re-emerged as a research area at the intersection of chemistry and machine learning.
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.
