Search Results for author:
Found 13 papers, 6 papers with code
Augmented Memory: Capitalizing on Experience Replay to Accelerate De Novo Molecular Design
Molecular generative models have shown remarkable sample efficiency when coupled with reinforcement learning, as demonstrated in the Practical Molecular Optimization (PMO) benchmark.
ChemCrow: Augmenting large-language models with chemistry tools
Large-language models (LLMs) have recently shown strong performance in tasks across domains, but struggle with chemistry-related problems.
GAUCHE: A Library for Gaussian Processes in Chemistry
By defining such kernels in GAUCHE, we seek to open the door to powerful tools for uncertainty quantification and Bayesian optimisation in chemistry.
SELFIES and the future of molecular string representations
We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science.
Identification of Enzymatic Active Sites with Unsupervised Language Modeling
Herein, we apply a Transformer architecture to a language representation of bio-catalyzed chemical reactions to learn the signal at the base of the substrate-active site atomic interactions.
Human-in-the-loop for a Disconnection Aware Retrosynthesis
In this work, we design an approach to provide this option by adapting a transformer-based model for single-step retrosynthesis.
Dataset Bias in the Natural Sciences: A Case Study in Chemical Reaction Prediction and Synthesis Design
Datasets in the Natural Sciences are often curated with the goal of aiding scientific understanding and hence may not always be in a form that facilitates the application of machine learning.
Unassisted Noise Reduction of Chemical Reaction Data Sets
Existing deep learning models applied to reaction prediction in organic chemistry can reach high levels of accuracy (> 90% for Natural Language Processing-based ones).
Mapping the Space of Chemical Reactions Using Attention-Based Neural Networks
Organic reactions are usually assigned to classes containing reactions with similar reagents and mechanisms.
Exploring Chemical Space using Natural Language Processing Methodologies for Drug Discovery
Text-based representations of chemicals and proteins can be thought of as unstructured languages codified by humans to describe domain-specific knowledge.
Predicting retrosynthetic pathways using a combined linguistic model and hyper-graph exploration strategy
We present an extension of our Molecular Transformer architecture combined with a hyper-graph exploration strategy for automatic retrosynthesis route planning without human intervention.
Molecular Transformer - A Model for Uncertainty-Calibrated Chemical Reaction Prediction
Organic synthesis is one of the key stumbling blocks in medicinal chemistry.
"Found in Translation": Predicting Outcomes of Complex Organic Chemistry Reactions using Neural Sequence-to-Sequence Models
With this approach, we demonstrate results superior to the state-of-the-art solution by a significant margin on the top-1 accuracy.
