Search Results for author:
Found 10 papers, 5 papers with code
Navigating the Maize: Cyclic and conditional computational graphs for molecular simulation
Many computational chemistry and molecular simulation workflows can be expressed as graphs.
Utilizing Reinforcement Learning for de novo Drug Design
Deep learning-based approaches for generating novel drug molecules with specific properties have gained a lot of interest in the last few years.
Industry-Scale Orchestrated Federated Learning for Drug Discovery
To apply federated learning to drug discovery we developed a novel platform in the context of European Innovative Medicines Initiative (IMI) project MELLODDY (grant n{\deg}831472), which was comprised of 10 pharmaceutical companies, academic research labs, large industrial companies and startups.
Autonomous Drug Design with Multi-Armed Bandits
Recent developments in artificial intelligence and automation support a new drug design paradigm: autonomous drug design.
Implications of Topological Imbalance for Representation Learning on Biomedical Knowledge Graphs
We provide support for this observation across different datasets, models as well as predictive tasks.
Parallel Capsule Networks for Classification of White Blood Cells
We applied our concept to the two current types of CapsNet architectures, studying the performance for networks with different layers of capsules.
Understanding the Performance of Knowledge Graph Embeddings in Drug Discovery
Knowledge Graphs (KG) and associated Knowledge Graph Embedding (KGE) models have recently begun to be explored in the context of drug discovery and have the potential to assist in key challenges such as target identification.
A Review of Biomedical Datasets Relating to Drug Discovery: A Knowledge Graph Perspective
We aim to help guide machine learning and KG practitioners who are interested in applying new techniques to the drug discovery field, but who may be unfamiliar with the relevant data sources.
Application of generative autoencoder in de novo molecular design
A major challenge in computational chemistry is the generation of novel molecular structures with desirable pharmacological and physiochemical properties.
Molecular De Novo Design through Deep Reinforcement Learning
This work introduces a method to tune a sequence-based generative model for molecular de novo design that through augmented episodic likelihood can learn to generate structures with certain specified desirable properties.
