143 citations · 343 across the 8 of their papers we have counts for
9 papers
Utilizing Reinforcement Learning for de novo Drug Design
Hampus Gummesson Svensson, Christian Tyrchan, Ola Engkvist +1
Deep learning-based approaches for generating novel drug molecules with specific properties have gained a lot of interest in the last few years. Recent studies have demonstrated pr…
Industry-Scale Orchestrated Federated Learning for Drug Discovery
Martijn Oldenhof, Gergely Ács, Balázs Pejó +44
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°831472), whic…
Autonomous Drug Design with Multi-Armed Bandits
Hampus Gummesson Svensson, Esben Jannik Bjerrum, Christian Tyrchan +2
Recent developments in artificial intelligence and automation support a new drug design paradigm: autonomous drug design. Under this paradigm, generative models can provide suggest…
Implications of Topological Imbalance for Representation Learning on Biomedical Knowledge Graphs
Stephen Bonner, Ufuk Kirik, Ola Engkvist +2
Adoption of recently developed methods from machine learning has given rise to creation of drug-discovery knowledge graphs (KG) that utilize the interconnected nature of the domain…
Parallel Capsule Networks for Classification of White Blood Cells
Juan P. Vigueras-Guillén, Arijit Patra, Ola Engkvist +1
Capsule Networks (CapsNets) is a machine learning architecture proposed to overcome some of the shortcomings of convolutional neural networks (CNNs). However, CapsNets have mainly…
Understanding the Performance of Knowledge Graph Embeddings in Drug Discovery
Stephen Bonner, Ian P Barrett, Cheng Ye +4
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…
A Review of Biomedical Datasets Relating to Drug Discovery: A Knowledge Graph Perspective
Stephen Bonner, Ian P Barrett, Cheng Ye +5
Drug discovery and development is a complex and costly process. Machine learning approaches are being investigated to help improve the effectiveness and speed of multiple stages of…
Application of generative autoencoder in de novo molecular design
Thomas Blaschke, Marcus Olivecrona, Ola Engkvist +2
A major challenge in computational chemistry is the generation of novel molecular structures with desirable pharmacological and physiochemical properties. In this work, we investig…
Molecular De Novo Design through Deep Reinforcement Learning
Marcus Olivecrona, Thomas Blaschke, Ola Engkvist +1
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 wit…