Ola Engkvist
Visar 39 publikationer
Temporal Evaluation of Probability Calibration with Experimental Errors
Publishing neural networks in drug discovery might compromise training data privacy
Towards Interpretable Models of Chemist Preferences for Human-in-the-Loop Assisted Drug Discovery
Temporal Evaluation of Uncertainty Quantification Under Distribution Shift
Enhancing uncertainty quantification in drug discovery with censored regression labels
Registries in Machine Learning-Based Drug Discovery: A Shortcut to Code Reuse
Utilizing reinforcement learning for de novo drug design
Scoring the synthesizability of any novel non-natural amino acid
QSARtuna: An Automated QSAR Modeling Platform for Molecular Property Prediction in Drug Design
Evaluation of reinforcement learning in transformer-based molecular design
Human-in-the-loop active learning for goal-oriented molecule generation
Expanding the chemical space using a chemical reaction knowledge graph
Navigating the Maize: cyclic and conditional computational graphs for molecular simulation
Metis: a python-based user interface to collect expert feedback for generative chemistry models
Exhaustive local chemical space exploration using a transformer model
De novo generated combinatorial library design
Industry-Scale Orchestrated Federated Learning for Drug Discovery
Link-INVENT: generative linker design with reinforcement learning
Transformer-based molecular optimization beyond matched molecular pairs
Autonomous Drug Design with Multi-Armed Bandits
Evaluation guidelines for machine learning tools in the chemical sciences
Icolos: a workflow manager for structure-based post-processing of de novo generated small molecules
De Novo Drug Design Using Reinforcement Learning with Graph- Based Deep Generative Models
Implications of topological imbalance for representation learning on biomedical knowledge graphs
A review of biomedical datasets relating to drug discovery: a knowledge graph perspective
LibINVENT: Reaction-based Generative Scaffold Decoration for in Silico Library Design
Exploring Graph Traversal Algorithms in Graph-Based Molecular Generation
Prediction of the Chemical Context for Buchwald-Hartwig Coupling Reactions
Using Active Learning to Develop Machine Learning Models for Reaction Yield Prediction
Improving de novo molecular design with curriculum learning
Human-in-the-loop assisted de novo molecular design
DockStream: a docking wrapper to enhance de novo molecular design
Parallel Capsule Networks for Classification of White Blood Cells
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