Conformational energy penalties of protein-bound ligands. - Jonas Boström; Per-Ola Norrby; Tommy Liljefors (J. Comput. Aided Mol. Des., 1998)

A pharmacophore model for dopamine D4 receptor antagonists. - Jonas Boström; Klaus Gundertofte; Tommy Liljefors (J. Comput. Aided Mol. Des., 2000)

Reproducing the conformations of protein-bound ligands: A critical evaluation of several popular conformational searching tools. - Jonas Boström (J. Comput. Aided Mol. Des., 2001)

A 3D QSAR Study on a Set of Dopamine D4 Receptor Antagonists. - Jonas Boström; Markus Böhm; Klaus Gundertofte; Gerhard Klebe (J. Chem. Inf. Comput. Sci., 2003)

MIMUMBA Revisited: Torsion Angle Rules for Conformer Generation Derived from X-ray Structures. - Jens Sadowski; Jonas Boström (J. Chem. Inf. Model., 2006)

A NEMO potential that includes the dipole-quadrupole and quadrupole-quadrupole polarizability. - Asbjørn Holt; Jonas Boström; Gunnar Karlström; Roland Lindh (J. Comput. Chem., 2010)

Exploiting Structural Information in Patent Specifications for Key Compound Prediction. - Christian Tyrchan; Jonas Boström; Fabrizio Giordanetto; Jon Winter; Sorel Muresan (J. Chem. Inf. Model., 2012)

A new module for constrained multi-fragment geometry optimization in internal coordinates implemented in the MOLCAS package. - Victor Vysotskiy; Jonas Boström; Valera Veryazov (J. Comput. Chem., 2013)

What compound should I make next? Using Matched Molecular Series for prospective medicinal chemistry. - Noel M. O'Boyle; Roger A. Sayle; Jonas Boström (J. Cheminformatics, 2014)

Molecular Rift: Virtual Reality for Drug Designers. - Magnus Norrby; Christoph Grebner; Joakim Eriksson; Jonas Boström (J. Chem. Inf. Model., 2015)

Discovery and Evaluation of Anti-Fibrinolytic Plasmin Inhibitors Derived from 5-(4-Piperidyl)isoxazol-3-ol (4-PIOL). - Thomas C. Schmidt; Per-Olof Eriksson; David Gustafsson; David Cosgrove; Bente Frølund; Jonas Boström (J. Chem. Inf. Model., 2017)

Improving the Use of Deep Convolutional Neural Networks for the Prediction of Molecular Properties. - Niclas Ståhl; Göran Falkman; Alexander Karlsson; Gunnar Mathiason; Jonas Boström (PACBB, 2018)

Deep Reinforcement Learning for Multiparameter Optimization in de novo Drug Design. - Niclas Ståhl; Göran Falkman; Alexander Karlsson; Gunnar Mathiason; Jonas Boström (J. Chem. Inf. Model., 2019)

Deep Convolutional Neural Networks for the Prediction of Molecular Properties: Challenges and Opportunities Connected to the Data. - Niclas Ståhl; Göran Falkman; Alexander Karlsson; Gunnar Mathiason; Jonas Boström (J. Integr. Bioinform., 2019)

Transformers for future medicinal chemists. - Jonas Boström (Nat. Mach. Intell., 2021)

Reusability report: Learning the language of synthetic methods used in medicinal chemistry. - Jon Paul Janet; Anna Tomberg; Jonas Boström (Nat. Mach. Intell., 2021)

On the Value of Using 3D Shape and Electrostatic Similarities in Deep Generative Methods. - Giovanni Bolcato; Esther Heid; Jonas Boström (J. Chem. Inf. Model., 2022)