Ola Engkvist
Showing 66 publications
Improving protein-ligand complex generation with force field guidance
MolEncoder: Improved Masked Language Modeling for Molecules
Democratising real-world drug discovery through agentic AI
Machine-Learned Electrostatic Potentials for Accurate Hydration Free Energy Calculations
Temporal Evaluation of Probability Calibration with Experimental Errors
Diversity-Aware Reinforcement Learning for de novo Drug Design
Network Analysis of the Organic Chemistry in Patents, Literature, and Pharmaceutical Industry
PepINVENT: generative peptide design beyond natural amino acids
Towards Interpretable Models of Chemist Preferences for Human-in-the-Loop Assisted Drug Discovery
Temporal Evaluation of Uncertainty Quantification Under Distribution Shift
WEISS: Wasserstein efficient sampling strategy for LLMs in drug design
Publishing neural networks in drug discovery might compromise training data privacy
Diversity-Aware Reinforcement Learning for de novo Drug Design
Enhancing uncertainty quantification in drug discovery with censored regression labels
Protein-ligand data at scale to support machine learning
MolEncoder: towards optimal masked language modeling for molecules
Boltzmann priors for Implicit Transfer Operators
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
A data science roadmap for open science organizations engaged in early-stage drug discovery
Evaluation of reinforcement learning in transformer-based molecular design
Navigating the Maize: cyclic and conditional computational graphs for molecular simulation
Exhaustive local chemical space exploration using a transformer model
Human-in-the-loop active learning for goal-oriented molecule generation
QSARtuna: An Automated QSAR Modeling Platform for Molecular Property Prediction in Drug Design
Metis: a python-based user interface to collect expert feedback for generative chemistry models
Expanding the chemical space using a chemical reaction knowledge graph
De novo generated combinatorial library design
Industry-Scale Orchestrated Federated Learning for Drug Discovery
Diverse Data Expansion with Semi-Supervised k-Determinantal Point Processes
Link-INVENT: generative linker design with reinforcement learning
Autonomous Drug Design with Multi-Armed Bandits
Transformer-based molecular optimization beyond matched molecular pairs
A review of biomedical datasets relating to drug discovery: a knowledge graph perspective
Novel endosomolytic compounds enable highly potent delivery of antisense oligonucleotides
Icolos: a workflow manager for structure-based post-processing of de novo generated small molecules
Evaluation guidelines for machine learning tools in the chemical sciences
De Novo Drug Design Using Reinforcement Learning with Graph- Based Deep Generative Models
Implications of topological imbalance for representation learning on biomedical knowledge graphs
LibINVENT: Reaction-based Generative Scaffold Decoration for in Silico Library Design
Exploring Graph Traversal Algorithms in Graph-Based Molecular Generation
Using Active Learning to Develop Machine Learning Models for Reaction Yield Prediction
Human-in-the-loop assisted de novo molecular design
Improving de novo molecular design with curriculum learning
Prediction of the Chemical Context for Buchwald-Hartwig Coupling Reactions
Graph networks for molecular design
DockStream: a docking wrapper to enhance de novo molecular design
Artificial intelligence and automation in computer aided synthesis planning
Parallel Capsule Networks for Classification of White Blood Cells
Uncertainty quantification in drug design
Practical notes on building molecular graph generative models
A de novo molecular generation method using latent vector based generative adversarial network
AI-assisted synthesis prediction
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