PaccMann: Prediction of anticancer compound sensitivity with multi-modal attention-based neural networks. - Ali Oskooei; Jannis Born; Matteo Manica; Vigneshwari Subramanian; Julio Sáez-Rodríguez; María Rodríguez Martínez (CoRR, 2018)

Towards Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-based Convolutional Encoders. - Matteo Manica; Ali Oskooei; Jannis Born; Vigneshwari Subramanian; Julio Sáez-Rodríguez; María Rodríguez Martínez (CoRR, 2019)

Reinforcement learning-driven de-novo design of anticancer compounds conditioned on biomolecular profiles. - Jannis Born; Matteo Manica; Ali Oskooei; María Rodríguez Martínez (CoRR, 2019)

AI Enables Explainable Drug Sensitivity Screenings. - Matteo Manica; Ali Oskooei; Jannis Born (ERCIM News, 2019)

CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models. - Vijil Chenthamarakshan; Payel Das; Samuel C. Hoffman; Hendrik Strobelt; Inkit Padhi; Kar Wai Lim; Benjamin Hoover; Matteo Manica; Jannis Born; Teodoro Laino; Aleksandra Mojsilovic (NeurIPS, 2020)

PaccMannRL: Designing Anticancer Drugs From Transcriptomic Data via Reinforcement Learning. - Jannis Born; Matteo Manica; Ali Oskooei; Joris Cadow; María Rodríguez Martínez (RECOMB, 2020)

COVID-19 Control by Computer Vision Approaches: A Survey. - Anwaar Ulhaq; Jannis Born; Asim Khan; Douglas P. S. Gomes; Subrata Chakraborty; Manoranjan Paul (IEEE Access, 2020)

POCOVID-Net: Automatic Detection of COVID-19 From a New Lung Ultrasound Imaging Dataset (POCUS). - Jannis Born; Gabriel Brändle; Manuel Cossio; Marion Disdier; Julie Goulet; Jérémie Roulin; Nina Wiedemann (CoRR, 2020)

PaccMannRL on SARS-CoV-2: Designing antiviral candidates with conditional generative models. - Jannis Born; Matteo Manica; Joris Cadow; Greta Markert; Nil Adell Mill; Modestas Filipavicius; María Rodríguez Martínez (CoRR, 2020)

Accelerating COVID-19 Differential Diagnosis with Explainable Ultrasound Image Analysis. - Jannis Born; Nina Wiedemann; Gabriel Brändle; Charlotte Buhre; Bastian Rieck; Karsten M. Borgwardt (CoRR, 2020)

PaccMann: a web service for interpretable anticancer compound sensitivity prediction. - Joris Cadow; Jannis Born; Matteo Manica; Ali Oskooei; María Rodríguez Martínez (Nucleic Acids Res., 2020)

Lessons Learned from the Development and Application of Medical Imaging-Based AI Technologies for Combating COVID-19: Why Discuss, What Next. - Maria Gabrani; Ender Konukoglu; David Beymer; Gustavo Carneiro 001; Jannis Born; Michal Guindy; Michal Rosen-Zvi (CLIP/DCL/LL-COVID19/PPML@MICCAI, 2021)

TITAN: T-cell receptor specificity prediction with bimodal attention networks. - Anna Weber; Jannis Born; María Rodríguez Martínez (Bioinform., 2021)

Data-driven molecular design for discovery and synthesis of novel ligands: a case study on SARS-CoV-2. - Jannis Born; Matteo Manica; Joris Cadow; Greta Markert; Nil Adell Mill; Modestas Filipavicius; Nikita Janakarajan; Antonio Cardinale; Teodoro Laino; María Rodríguez Martínez (Mach. Learn. Sci. Technol., 2021)

On the role of artificial intelligence in medical imaging of COVID-19. - Jannis Born; David Beymer; Deepta Rajan; Adam Coy; Vandana V. Mukherjee; Matteo Manica; Prasanth Prasanna; Deddeh Ballah; Michal Guindy; Dorith Shaham; Pallav L. Shah; Emmanouil Karteris; et al. (Patterns, 2021)

A Fully Differentiable Set Autoencoder. - Nikita Janakarajan; Jannis Born; Matteo Manica (KDD, 2022)

A computational investigation of inventive spelling and the "Lesen durch Schreiben" method. - Jannis Born; Nikola I. Nikolov; Anna Rosenkranz; Alfred Schabmann; Barbara Maria Schmidt (Comput. Educ. Artif. Intell., 2022)

Regression Transformer: Concurrent Conditional Generation and Regression by Blending Numerical and Textual Tokens. - Jannis Born; Matteo Manica (CoRR, 2022)

GT4SD: Generative Toolkit for Scientific Discovery. - Matteo Manica; Joris Cadow; Dimitrios Christofidellis; Ashish Dave; Jannis Born; Dean Clarke; Yves Gaetan Nana Teukam; Samuel C. Hoffman; Matthew Buchan; Vijil Chenthamarakshan; Timothy Donovan; Hsiang-Han Hsu; et al. (CoRR, 2022)

Active Site Sequence Representations of Human Kinases Outperform Full Sequence Representations for Affinity Prediction and Inhibitor Generation: 3D Effects in a 1D Model. - Jannis Born; Tien Huynh; Astrid Stroobants; Wendy D. Cornell; Matteo Manica (J. Chem. Inf. Model., 2022)

On the Choice of Active Site Sequences for Kinase-Ligand Affinity Prediction. - Jannis Born; Yoel Shoshan; Tien Huynh; Wendy D. Cornell; Eric J. Martin; Matteo Manica (J. Chem. Inf. Model., 2022)

Accelerating Molecular Discovery with Generative Language Models: A journey through the chemical space. - Jannis Born (2022)

Unifying Molecular and Textual Representations via Multi-task Language Modelling. - Dimitrios Christofidellis; Giorgio Giannone; Jannis Born; Ole Winther; Teodoro Laino; Matteo Manica (ICML, 2023)

Domain-agnostic and Multi-level Evaluation of Generative Models. - Girmaw Abebe Tadesse; Jannis Born; Celia Cintas; William Ogallo; Dmitry Zubarev; Matteo Manica; Komminist Weldemariam (CoRR, 2023)

Language models in molecular discovery. - Nikita Janakarajan; Tim Erdmann; Sarath Swaminathan; Teodoro Laino; Jannis Born (CoRR, 2023)

Regression Transformer enables concurrent sequence regression and generation for molecular language modelling. - Jannis Born; Matteo Manica (Nat. Mac. Intell., 2023)

Quantum Theory and Application of Contextual Optimal Transport. - Nicola Mariella; Albert Akhriev; Francesco Tacchino; Christa Zoufal; Juan Carlos Gonzalez-Espitia; Benedek Harsanyi; Eugene Koskin; Ivano Tavernelli; Stefan Woerner; Marianna Rapsomaniki; Sergiy Zhuk; Jannis Born (ICML, 2024)

Regress, Don't Guess - A Regression-like Loss on Number Tokens for Language Models. - Jonas Zausinger; Lars Pennig; Kacper Chlodny; Vincent Limbach; Anna Ketteler; Thorben Prein; Vishwa Mohan Singh; Michael Morris Danziger; Jannis Born (CoRR, 2024)

Multiscale Byte Language Models - A Hierarchical Architecture for Causal Million-Length Sequence Modeling. - Eric Egli; Matteo Manica; Jannis Born (CoRR, 2025)

We Need Improved Data Curation and Attribution in AI for Scientific Discovery. - Mara Graziani; Antonio Foncubierta; Dimitrios Christofidellis; Irina Espejo Morales; Malina Molnar; Marvin Alberts; Matteo Manica; Jannis Born (CoRR, 2025)

Towards generalizable single-cell perturbation modeling via the Conditional Monge Gap. - Alice Driessen; Benedek Harsanyi; Marianna Rapsomaniki; Jannis Born (CoRR, 2025)

Quantum Doubly Stochastic Transformers. - Jannis Born; Filip Skogh; Kahn Rhrissorrakrai; Filippo Utro; Nico Wagner; Aleksandros Sobczyk (CoRR, 2025)