Ola Engkvist

AstraZeneca (Singapore) — Singapore

Computer Science · Physical Sciences

54h-index15.0kcitations420works7.62yr avg
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Research Topics

Computational Drug Discovery Methods(290), Machine Learning in Materials Science(180), Protein Structure and Dynamics(48), Chemical Synthesis and Analysis(45), Analytical Chemistry and Chromatography(26)

Publications419 total

Machine-Learned Electrostatic Potentials for Accurate Hydration Free Energy Calculations
arXiv (Cornell University)·2025Open Access
MolEncoder: Improved Masked Language Modeling for Molecules
Lecture notes in computer science·2025· 1 cited
Practically Significant Method Comparison Protocols for Machine Learning in Small Molecule Drug Discovery
Journal of Chemical Information and Modeling·2025· 21 citedOpen Access
Diversity-Aware Reinforcement Learning for de novo Drug Design
2025
MolEncoder: Towards Optimal Masked Language Modeling for Molecules
2025Open Access
Author response for "From concept to chemistry: integrating protection group strategy and reaction feasibility into non-natural amino acid synthesis planning"
2025
Protein–ligand data at scale to support machine learning
Nature Reviews Chemistry·2025· 7 cited
Temporal distribution shift in real-world pharmaceutical data: Implications for uncertainty quantification in QSAR models
Artificial Intelligence in the Life Sciences·2025· 2 citedOpen Access
From Concept to Chemistry: Integrating protection group strategy and reaction feasibility into non-natural amino acid synthesis planning
2025Open Access
Network Analysis of the Organic Chemistry in Patents, Literature, and Pharmaceutical Industry
Molecular Informatics·2025Open Access
Exploring Modularity of Agentic Systems for Drug Discovery
arXiv (Cornell University)·2025Open Access
Diverse Mini-Batch Selection in Reinforcement Learning for Efficient Chemical Exploration in de novo Drug Design
arXiv (Cornell University)·2025Open Access
WEISS: Wasserstein efficient sampling strategy for LLMs in drug design
Machine Learning Science and Technology·2025Open Access
E-GuARD: expert-guided augmentation for the robust detection of compounds interfering with biological assays
Journal of Cheminformatics·2025· 1 citedOpen Access
An Expert-Augmented Deep Learning Approach for Synthesis Route Evaluation
2025· 1 citedOpen Access
Publishing neural networks in drug discovery might compromise training data privacy
Journal of Cheminformatics·2025· 1 citedOpen Access
Achieving well-informed decision-making in drug discovery: a comprehensive calibration study using neural network-based structure-activity models
Journal of Cheminformatics·2025· 2 citedOpen Access
Enhancing uncertainty quantification in drug discovery with censored regression labels
Artificial Intelligence in the Life Sciences·2025· 4 citedOpen Access
An Expert-Augmented Deep Learning Approach for Synthesis Route Evaluation
2025· 3 citedOpen Access
Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models
arXiv (Cornell University)·2025Open Access
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Frequent Co-authors

Hongming Chen(19), Esben Jannik Bjerrum(12), Jean‐Louis Reymond(8), Thomas Blaschke(7), Amol Thakkar(6), Josep Arús‐Pous(6), Christian Tyrchan(6), Atanas Patronov(6), Simon Johansson(5), Lewis Mervin(5), Marcus Olivecrona(4), Kostas Papadopoulos(4), Thierry Kogej(4), Rocío Mercado(3), Christian Margreitter(3), Jürgen Bajorath(3), Hugo Ceulemans(3), Jiazhen He(3), Jon Paul Janet(3), Pavel Hobza(3)