Engkvist, Ola

The rise of deep learning in drug discovery

Chen, H., Engkvist, O., Wang, Y., Olivecrona, M., & Blaschke, T. (2018) · Drug Discovery Today

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 1,770

Molecular representations in AI-driven drug discovery: a review and practical guide

David, L., Thakkar, A., Mercado, R., & Engkvist, O. (2020) · Journal of Cheminformatics

Computational Drug Discovery MethodsGraph theory and applications

Cited by 587

REINVENT 2.0: An AI Tool for De Novo Drug Design

Blaschke, T., Arús-Pous, J., Chen, H., Margreitter, C., Tyrchan, C., Engkvist, O., Papadopoulos, K., & Patronov, A. (2020) · Journal of Chemical Information and Modeling

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 496

Application of Generative Autoencoder in De Novo Molecular Design

Blaschke, T., Olivecrona, M., Engkvist, O., Bajorath, J., & Chen, H. (2018) · Molecular Informatics

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 411

On the Integration of In Silico Drug Design Methods for Drug Repurposing

March-Vila, E., Pinzi, L., Sturm, N., Tinivella, A., Engkvist, O., Chen, H., & Rastelli, G. (2017) · Frontiers in Pharmacology

Computational Drug Discovery MethodsProtein Degradation and Inhibitors

Cited by 235

Link-INVENT: Generative Linker Design with Reinforcement Learning

Guo, J., Knuth, F., Margreitter, C., Janet, J. P., Papadopoulos, K., Engkvist, O., & Patronov, A. (2022) · Digital Discovery

Computational Drug Discovery MethodsProtein Degradation and Inhibitors

Cited by 101

Cell Morphological Profiling Enables High-Throughput Screening for PROteolysis TArgeting Chimera (PROTAC) Phenotypic Signature

Trapotsi, M.-A., Mouchet, E., Williams, G., Monteverde, T., Juhani, K., Turkki, R., Miljković, F., Martinsson, A., Mervin, L., Pryde, K. R., et al. (2022) · ACS Chemical Biology

Protein Degradation and InhibitorsCell Image Analysis Techniques

Cited by 51

Small molecule inducers of ABCA1 and apoE that act through indirect activation of the LXR pathway

Fan, J., Zhao, R. Q., Parro, C., Zhao, W., Chou, H.-Y., Robert, J., Deeb, T. Z., Raynoschek, C., Barichievy, S., Engkvist, O., et al. (2018) · Journal of Lipid Research

Cholesterol and Lipid MetabolismAlzheimer's disease research and treatments

Cited by 50

Axl receptor tyrosine kinase is a regulator of apolipoprotein E

Zhao, W., Fan, J., Kulic, I., Koh, C., Clark, A., Meuller, J., Engkvist, O., Barichievy, S., Raynoschek, C., Hicks, R., et al. (2020) · Molecular Brain

Alzheimer's disease research and treatmentsNeuroinflammation and Neurodegeneration Mechanisms

Cited by 25

Democratising real-world drug discovery through agentic AI

He, J., Lai, H., Saigiridharan, L., Ghiandoni, G. M., Jenei, K., Gokalp, U., Nuković, A., Engkvist, O., Janet, J. P., & Genheden, S. (2026) · Drug Discovery Today

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 17

E-GuARD: Expert-Guided Augmentation for Robust Interference Compound Detection

Palmacci, V., Nahal, Y., Welsch, M., Engkvist, O., Kaski, S., & Kirchmair, J. (2025) · Journal of Cheminformatics

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 6

E-GuARD: expert-guided augmentation for the robust detection of compounds interfering with biological assays

Palmacci, V., Nahal, Y., Welsch, M., Engkvist, O., Kaski, S., & Kirchmair, J. (2025) · Journal of Cheminformatics

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 6