Search Results for author:
Found 12 papers, 5 papers with code
Unifying Molecular and Textual Representations via Multi-task Language Modelling
Here, we propose a multi-domain, multi-task language model to solve a wide range of tasks in both the chemical and natural language domains.
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.
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.
CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models
CogMol also includes insilico screening for assessing toxicity of parent molecules and their metabolites with a multi-task toxicity classifier, synthetic feasibility with a chemical retrosynthesis predictor, and target structure binding with docking simulations.
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.
An Information Extraction and Knowledge Graph Platform for Accelerating Biochemical Discoveries
Information extraction and data mining in biochemical literature is a daunting task that demands resource-intensive computation and appropriate means to scale knowledge ingestion.
Comparison of computational methods for the electrochemical stability window of solid-state electrolyte materials
Superior stability and safety are key promises attributed to all-solid-state batteries (ASSBs) containing solid-state electrolyte (SSE) compared to their conventional counterparts utilizing liquid electrolyte.
Materials Science Chemical Physics
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.
